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Effects of Motivation, Preferred Learning Styles, and Perceptions of Classroom Climate on Achievement in Ninth and Tenth...


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1 EFFECTS OF MOTIVATION, PREFERRED LEARNING STYLES, AND PERCEPTIONS OF CLASSROOM CLIMATE ON ACHIEVEMENT IN NINTH AND TENTH GRADE MATH STUDENTS By SUSAN E. DAVIS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2 2007 Susan E. Davis

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3 To My Husband

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4 ACKNOWLEDGMENTS I would like to thank my chair and mentor, Dr. Thomas A. Oakland, whose guidance and support have contributed greatly to my present achievement and to my future potential. I would also like to thank my husband, Jack, and my s on, Jim, whose love and support sustain me. Finally, I would like to thank my mother, Elean or, whose own success has been my inspiration.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES................................................................................................................ .........8 ABSTRACT....................................................................................................................... ..............9 CHAPTER 1 REVIEW OF THE LITERATURE........................................................................................10 Classroom Climate.............................................................................................................. ....10 Learning Styles Preferences...................................................................................................17 Temperament...................................................................................................................17 Temperament Theories....................................................................................................19 Hippocrates...............................................................................................................19 Immanuel Kant.........................................................................................................20 William James..........................................................................................................20 Wilhelm Wundt........................................................................................................20 Carl Jung..................................................................................................................21 Myers-Briggs............................................................................................................22 Student styles questionnaire.....................................................................................23 Motivation..................................................................................................................... ..........28 Expectancy-Value Theory...............................................................................................30 Attribution Theory...........................................................................................................31 Self-determination Theory...............................................................................................32 Goal Achievement Theory..............................................................................................32 Goal Orientation Theory..................................................................................................33 Aptitude and Achievement.....................................................................................................35 Aptitude....................................................................................................................... ....35 Academic aptitude...........................................................................................................36 Achievement....................................................................................................................36 Proposal....................................................................................................................... ...........38 Hypotheses..................................................................................................................... .........38 Study One...................................................................................................................... ..39 Study Two...................................................................................................................... .41 2 MATERIALS AND METHODS...........................................................................................43 Participants................................................................................................................... ..........43 Instrumentation................................................................................................................ .......44 Classroom Climate..........................................................................................................44 Learning Style Preferences..............................................................................................45

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6 Motivation..................................................................................................................... ..46 Academic Aptitude..........................................................................................................46 Achievement....................................................................................................................47 Procedure...................................................................................................................... ..........47 3 RESULTS........................................................................................................................ .......53 Preliminary Analysis of Data..................................................................................................53 Gender Effects.................................................................................................................53 Grade Level Effects.........................................................................................................53 Race/Ethnicity and Family Income Effects.....................................................................55 Teacher and Class Period Effects....................................................................................57 Study One...................................................................................................................... .........57 Students Ratings of Classroom Clim ate Will Predict Math Achievement....................57 Students Ratings of Motivation W ill Predict Math Achievement.................................58 Study Two...................................................................................................................... .........59 Students Preferred Learning Styles Will Predict Math Achievement............................59 Contribution of a Confluence of Variab les to Math Achievement beyond that Obtained Individually..................................................................................................60 4 DISCUSSION..................................................................................................................... ....62 Hypotheses..................................................................................................................... .........62 Students Ratings of Classroom Clim ate Will Predict Math Achievement....................62 Students Ratings of Motivation wi ll Predict Math Achievement..................................65 Students Preferred Learning Styles will Predict Math Achievement.............................65 Contribution of a Confluence of Variab les to Math Achievement Beyond that Obtained Individually..................................................................................................67 Implications................................................................................................................... .........69 5 LIMITATIONS AND FUTURE STUDIES...........................................................................74 APPENDIX A FORMS.......................................................................................................................... .........77 B MISCELLANEOUS TABLES...............................................................................................80 LIST OF REFERENCES............................................................................................................. ..81 BIOGRAPHICAL SKETCH.......................................................................................................98

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7 LIST OF TABLES Table page 1-1 Scales of the What Is Happening in This Class.................................................................. 15 1-2 Jungian typologies......................................................................................................... ....21 2-1 Participant demographic information................................................................................43 3-1 Gender differences......................................................................................................... ....53 3-2 Means and Standard Deviations by Grade.........................................................................54 3-3 Tests for effects between groups by race/ethnicity and income........................................56 3-4 Tests for effects between groups by parent education.......................................................56 3-5 Tests for effects between gr oups by teacher and class period...........................................57 3-6 Classroom climate full model for prediction of achievement............................................58 3-7 Classroom climate final model for the prediction of achievement....................................58 3-8 Motivation full model for the prediction of achievement..................................................58 3-9 Motivation final model for the prediction of achievement................................................59 3-10 Preferred learning styles full mode l for the prediction of achievement.............................59 3-11 Preferred learning styles final m odel for the prediction of achievement...........................60 3-12 Unique contribution of variables to the predication of achievement.................................60 3-13 Full model of confluence of variab les for the prediction of achievement.........................61 3-14 Final model of confluence of variables for the prediction of achievement.......................61 6-1 Descriptive statistics fo r full model of variables...............................................................80 6-2 Means and standard deviations for fi nal model of confluence of variables......................80

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8 LIST OF FIGURES Figure page 1-1 Relationship of constructs examined in this study.............................................................39 1-2 Relationships between classroom clim ate domains with math achievement.....................40 1-3 Contribution of classroom climate, motiva tion, and preferred learning styles to math achievement.................................................................................................................... ...40

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9 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy EFFECTS OF MOTIVATION, PREFERRED LEARNING STYLES, AND PERCEPTIONS OF CLASSROOM CLIMATE ON ACHIEVEMENT IN NINTH AND TENTH GRADE MATH STUDENTS By Susan E. Davis May 2007 Chair: Thomas A. Oakland Major: School Psychology One hundred three ninth and tenth grade al gebra students comple ted self-reports of motivation, classroom climate, and learning styles preferences. A nonverbal measure of aptitude and an algebra pretest was administered at the beginning of the academic year (August, 2007) and an algebra post test was administered at th e midpoint of the academic year (February, 2007). Results indicated self-reported levels of motivation were not significant predictors for achievement in algebra class. However, for clas sroom climate, students with lower ratings for classroom involvement and higher ratings of task orientation demonstrat ed higher increases in achievement than students with higher ratings of involvement and lower ratings of task orientation. Additionally, students displaying a thinking preference achieved high scores than student with demonstrating a fee ling preference. Results of th is study indicate students whose perceptions and preferences are more consistent with instructional style demonstrate higher short term gains in math than students with less congruent preferences and perceptions.

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10 CHAPTER 1 REVIEW OF THE LITERATURE Each individual brings into the classroom hi s or her own educational history and personal characteristics. These characteristics includ e ones sense of well-be ing, self-efficacy beliefs (House, 2002; Jackson, 2002), self-concept (C rohn, 1983), sense of belongingness (Goodenow, 1995; Osterman, 2000), satisfaction with soci al activities (Townsend & Hicks, 1997), interpersonal relationships (O sterman, 2000), and preferred lear ning styles (Lawrence, 1982). Learning style preferences refer to those met hods a child uses when receiving and processing information. These methods contribute to defi ning individual-environmen t interactions. Thus, students preferred learning styles may affect perceptions of their l earning environment, levels of motivation to engage, and their achievement. This study examines the impact of students preferred learning st yles, perceptions of classroom climate, and self-reported levels of mo tivation as they relate to academic achievement in tenth grade students. Co mpared to students reporting di screpancies between preferred learning styles and instructional methods, stude nts with a better stude nt-environment fit are expected to report more positive perceptions of classroom climate and higher levels of achievement motivation, resulting in hi gher levels of academic achievement. Classroom Climate Student achievement is influenced by f eelings of belongingness to their school environment (Deci, Vallerand, Pelletier, & Ryan, 1991; Osterman, 2000)). School belongingness refers to feelings of being accepte d and valued by their peers (Wilms, 2003a), whilst the participation component "is charac terized by factors such as school and class attendance, being prepared for class," and completing assignme nts (Wilms, 2003a). Those who report a higher feeling of belongi ngness with others display lowe r rates of emotional distress,

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11 drug abuse, violent behavior, criminal behavior, suicide, and school drop out rates (Battistich & Horn, 1997; Chipuer, Bramston, & Petty, 2003; Os terman, 2000; Resnick et al., 1997). Schools that promote belongingness, safety, security, mo rale, and parental and community involvement also promote lower drop out rates, higher attenda nce, greater levels of student engagement and motivation (Goodenow, 1993), school effort an d involvement (Anderman & Anderman, 1999), positive affect (Anderman, 1999) and improved educational outcomes (Alspaugh, 1998; Chipuer, Bramston, & Petty, 2003; Wilms, J., 2003; Robinson & Carrington, 2002; Rhodes & HoughtonHill, 2000; Wilms, 2003b). Children who feel invol ved within their school community are more likely to report a stronger sense of identity and autonomy, higher self-regulation, respect for authority, and a lower propensity to engage in deviant or negative behavior (Johnson, Lutzow, Strothoff, & Zannis, 1995; Kunc, 1992; Osterm an, 2000; Chipuer, Bramston, & Petty, 2003). Thus, the extent to which a school commun ity promotes belongingness affects student development, motivation, and achievement (Kunc, 1992; Osterman, 2000; Wilms, 2003a). The degree to which schools adopt policies and support practices designed to promote student engagement is measured through perceptions of school climat e. School climate refers to the psychosocial, academic, organizational, and cultural factors that comprise an education environment (Stringfield, 1994; Walberg, 1979) and are thought to influence student outcomes (Creemers, 1994; Fernandez, Cannon, & Chokshi 2003; Stewart & Brendefur, 2005). School climate indirectly affects classroom climate th rough the adopted policies and practices of the teachers (Ellis, 1996; Robinson & Carrington, 2002; Wilms, 2003b). Teacher practices, expectations (Crohn, 1983), crea tivity (Denny & Turn er, 1969), and stud ent interactions (Jacobson, 2000; Juarez, 2000; Wang et. al., 1993) help influence st udents perceptions of their classroom climate.

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12 Classroom climate encompasses all dimensi ons of classroom lif e (Wang, Haertel, & Walberg, 1993). The physical arrangement of furnitu re, availability of resource materials, length of class period (Chapin & Eastma n, 1996), level of task difficulty, type and pace of instruction (Wang et al., 1993), predictability of the envi ronment (Anderson, Stevens, Prawat, & Nickerson, 1987), and the value placed on interpersonal relationships (Gottfre dson & Gottfredson, 1989) influence classroom climate. Positive classr oom climates are safe and supportive and provide ample opportunities for explor ation and experimentation. Student perceptions of classroom climate ar e guided, in part, by individual values and expectations for success (Eccles, 198 3). Classroom climates that promote individual goal setting and provide choices for students are preferred by adolescents (Pin trich et al., 1994). Classrooms that value effort and foster positive feelings to ward learning promote mast ery of subject material (Ames & Archer, 1987; Gaith, 2003). Clear and structured rules (Key ser & Barling, 1981), focused, organized, and well planned lessons (P roctor, 1984), relevant curricula (Townsend & Hicks, 1995), explicit learning ob jectives, guided student practic e, frequent assessment, and positive feedback (Wang et al., 1993) promote deep er approaches to learning, resulting in higher achievement outcomes (Alspaugh, 1998; Dart, Burn ett, & Purdie, 2000; Haertel et al., 1981; McRobbie &Fraser, 1993). Associations between climate and outcome are inconsistent (Davis, Davis, & Smith, 2004). Although more positive ratings of classroom climate were associated with higher levels of math achievement (Davis, 2004; Davis, Davis, & Sm ith, 2004; Goh & Fraser, 1995), no associations were found for reading, science, or social studies. Studies of ge nder differences indicate girls tend to rate classroom climate more favor ably than boys (Townsend & Hicks, 1997).

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13 Cooperative goal structured classroom envir onments include those which promote group collaboration, performance and achievement, vers us competitive goal structured environments, which promote individual performance and ach ievement. Although cooperative learning was found to increase academic success for females (Gardner, Mason, & Matyas, 1989), Gaith (2003) found both males and females benefited. Howeve r, gender differences in classroom climate ratings or achievement were not evident in a study of middle school students (Davis, 2004; Davis, Davis, & Smith, 2004). Gender perceptions of teacher s upport indicate males tend to feel they are treated more sternly than females, yet believe teachers ha ve higher expectations for girls in academic performance (Myhill & Jones, 2006). Additiona lly, both boys and girls reported feeling that female teachers were more likely to treat bo th genders more fairly than male teachers. Teacher perceptions of students may impact st udent perceptions of classroom climate. For example, in a study of ethnically diverse cl assrooms, teachers tended to develop better relationships with students with similar et hnic backgrounds (Saft & Pianta, 2001). White teachers tended to underestimate aptitude and predictions of achievement for African American youth (Richman, Boelsky, Koovand, Vacca, & We st, 1997), call on and praise white youth compared to African American youth, and more likely to aid White st udents through the giving of clues for partial responses (Casteel, 1998). Th us, racial/ethnic factor s may influence minority students ratings of classroom climate for teacher support, cooperation and cohesiveness, as well as perceptions of involvement and equity. The assessment of classroom climate gene rally focuses on student interest and participation, interclassroom re lationships, support within the environment, emphasis on task completion, perceived task difficulty, interclassr oom competition, clarity and enforcement of

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14 rules and expectations, and th e overall environmental organization and management (Moos, 1974). According to Moos, climate refers to a groups impression of the social and psychological atmosphere of any social setting. The use of the Classr oom Environmental Scale (CES) (Moos, 1974) allows stude nts to rate the social c limate along four dimensions: relationships, personal development, system main tenance, and system change. Relationships refer to the types and intensity of relationships including those between t eacher-student, studentstudent, and staff-staff. They re flect the extent to which indivi duals within the environment are involved, helpful, open, and supportive. Pers onal development includes competition that emphasizes academic achievement and direction of personal growth and self-enhancement. System maintenance refers to organization and or derliness, including clar ity and consistency of classroom rules and teacher consistency. System change refers to the manner and facility of change within the classroom, and the vari ety and creativity of classroom activities. The CES was designed to measure releva nt aspects of conventional classroom environment. The Individualized Classroom E nvironment Inventory (ICEQ) (Fraser, 1986) was developed to measure qualities that help differentiate conventi onal classrooms from individualized settings. Although both instruments are useful in measuring different aspects of classroom climate, no one instrument included a comprehensive measure of classroom climate. The What Is Happening in This Class (W IHIC; Fraser, Fisher, & McRobbie, 1996; Aldridge & Fraser, 2000; Fraser, 1998) is one of the most recent and widely used learning environment instruments. The WIHIC was select ed due to the breadth of the measure across several qualities of classroom climate. The WIHIC measures students perception of their learning environment. The measure corresponds to other measures selected for this study in

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15 which students perceptions of th eir environment is examined in relation to achievement, rather than actual or unbiased measures of the classroom or classroom tasks. The WIHIC is designed for use with secondary classrooms and combines the most relevant scales from existing questionnaires The WIHIC consists of 7 scal es and 56 items scored using a five-point Likert scale. The seven scales include measures of student cohesiveness, teacher support, involvement, investigation, task orie ntation, cooperation and equity (Table 1-1). Table 1-1. Scales of the What Is Happening in This Class Student Cohesivenss The extent to whic h students are frie ndly and supportive of each other Teacher support The extent to which the teacher helps, befriends, and is interested in students. Involvement The extent to which st udents have attentive interest, participate in class, and are involved with other students in assessing the viability of new ideas. Investigation The extent to whic h classes emphasize skill building, inquiry, and their use in problem-solving and investigation. Task orientation The extent to wh ich completing planned activities and staying on the subject matter are important Cooperation The extent to which stud ents cooperate with each other during activities Equity The extent to which the teacher treats students equally, including distributing praise questions, and opportunities to be included in discussions Student cohesive refers to th e extent to which students ar e friendly and supportive of each other. An example of an item that assesses student cohesiveness is I make friends among students in this class. Teacher support refers to the extent to which teachers demonstrate interest in student success. An example of an item that assesses teacher support is The teacher takes a personal interest in me. Student involvement refers to the extent to which students contribute to class discussion. An example of an item that assesses involvement is I discuss my ideas in class. Investigation refe rs to the extent to which studen ts seek solutions to problems. An example of an item that assesses investigation is I carry out investigations to test my ideas.

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16 Task orientation refers to the extent to which st udents value task comple tion. An example of an item that assesses task orientation is Getting a certain amount of work done is important. Cooperation refers to the extent to which stud ents are involved with peers when completing tasks. An example of an item that assesses cooper ation is I cooperate w ith other students when doing assignment work. Equity refers to the extent to which students perceive their environment as fair and equitable. An example of an item that assesses equity is The teacher gives as much attention to my questi ons as to other students questions. Ratings of classroom climate involve the su bjective perceptions of students of their learning environment. Additionally, some scales can be used to compare perceptions with actual or preferred classroom characte ristics. Studies demonstrated a strong correlation between perceptions of climate and the actual climate of the classroo m (Allen & Fraser, 2002; Chionh & Fraser, 1998; Hunus & Fraser, 1997). However, this study examines individual differences in perceptions as they re late to achievement. Racial/ethnic and gender differences may fact or into ratings of perceived classroom climate. In a study by Kim, Fraser, and Fisher (2000), Korean students were assessed regarding their perceptions of the classroom climate and t eacher behavior. On all seven scales, boys and girls perceptions of the learni ng environment differed. Boys tended to rate Teacher Support, Involvement, Investigation, Task Orientation, an d Equity more positively than did girls. However, cultural differences, such as percepti on of teacher authority in Korea, make crosscultural comparisons difficult (Fisher & Rickards 1998). For example, in cross-cultural studies across Autstralia and Taiwan found higher rati ngs of classroom climate for Australians on Involvement, Investigation, Task Orientation, Cooperation, and Equity (Fraser and Aldridge, 1998).

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17 Cultural differences also were found for teac hers. Khine, M., & Fisher, D., (2001) found that teachers created different types of learning environment based upon the values of their culture. For example, while Asian cultures, such as Taiwan, value academic ability as the focus of education, teachers in Australia consider ed academic ability to be one of many aspects important to the learning experi ence. Socio-emotional development, considered important to Australian schools were consider ed more of a family responsib ility by the Taiwanese (Khine & Fisher). Differences between Chinese and American expectations for teache rs were also noted. Chinese teachers were more likely to prize enthusiasm and clarity in instructional styles while their American counterparts valued sensitivit y and patience (Steven & Stigler, 1992). Thus, these differences will be examined in this st udy. However, as this study compares individual climate perceptions to individua l achievement gains, no signifi cant differences are expected. Not only are elements in the classroom suggest ed to influence per ceptions of classroom climate, how students perceive their external world is influenced by temperament qualities. Learning Styles Preferences Temperament Temperament refers to the consistent, endur ing predispositions toward perspectives, preferences, affect, behavioral patterns, and environment interact ions from which an individual approaches his environment (Benson, 2005; Joyce, 2000; Kristal, 2005). Innate and stable (Goldsmith, Buss, Plomin, Rothbart, Thomas, Chess, Hinde, & McCall, 1987) temperament traits account for variance in mood, level of activity, and emotiona l response early in life (Chess & Thomas, 1996) and beyond (Thomas, Chess, & Birch, 1968). Individual differences in temperament are present early in life are believed to be biologically rooted and stable (Bates, 1989; Buss & Plomin, 1984) Temperament differences have been examined through early patterns of br ain electrical activity. For example, infants

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18 demonstrating high motor activity and high negative affect display greater activation in the right frontal region of the cortex. Infants demonstr ating high motor activity and high positive affect display greater activation in the left frontal region of the co rtex (Calkins & Fox, 1992). Patterns of brain electrical activity and patterns of behavior are consis tent in infancy (Calkins & Fox, 1992). Temperament traits have been found to have biological basis in brain structure and neurotransmittier levels. For example, temper ament traits associated with a propensity for depression have been linked to a short versi on of a gene's protein, known as the serotonin transporter protein. The short version is suggest ed to promote intense serotonin activity, thus degrading connections in the mood-regulation system (Bower, 2005). This indicates poorer communication response between ci ngulate activity and the amygda la. The amygdala regulates negative emotions such as fear responses. I ndividuals with poor re gulatory control of the amygdala by the cingulate activity demonstrate highe r levels of anxiety and increased sensitivity to negative environmental stimuli, such as stressful events. Other theories indicate differences in brai n functioning are temperament related. For example, Tomarken, Davidson, Wheeler, & Kinn ey (1992) found increased right prefrontal region activation was associated with an increased disposition for negative affect. Extreme right frontal electroencephalographic activ ity associated with higher cortisol levels were indicated in monkeys with increased fearful response compar ed with monkeys with extreme left frontal activity (Kalin, Larson, Shelton, Davidson, 1998). Studies linking temperament to biological dispositions are increa sing. A method known as quantitative trait loci (QTL) analysis enables the location and identification of chromosomal re gions involved in trai t variability (Mormede, Courvoisier, Ramos, Marissal-Ar vy, Ousova, Desautes, Duclos, Chaouloff, and Moisan, 2002).

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19 Temperament traits evolve as a result of developmental and environmental influence to comprise complex behavioral styles (Thomas et.al., 1968). For example, some children demonstrate low sensitivity and react readily to low intensity environm ental cues. They may appear to be overwhelmed easil y. Other children demonstrate hi gh sensitivity to environmental cues. They may appear more unaware, unobserva nt, or unconcerned with environmental cues. Thomas and colleagues (1968) describe three categories of infant temperament as easy, difficult, and slowtowarmup. Easy temperament describes traits that are flexible, adaptable, and sociable. Slow to warm up infants are describe d as slow to adapt to changes in environment, lower in activity level, somewhat hesitant with unfamiliar people or places. Difficult temperament describes traits that are resistan t to transitions and changes, withdrawn from unfamiliar environments, difficult to console, and intense negative mood. Although many infants are readily categorized within the thr ee trait paradigm, some infants demonstrate an interaction of these three. Categorization of temperament as inborn trai ts is not new. For centuries, man has attempted to classify, characterize, and explai n human behavior through temperament. Current efforts to describe and quantify temperament qua lities have their roots in early theories of temperament. Temperament Theories Hippocrates Some of the earliest theories developed in ancient Greece attempted to explain behavior through temperament styles that reflect individual s dispositions and perspectives. Temperament styles were thought to effect how one gathers, pr ocesses, and responds to stimulus. For example, Hippocrates, considered the father of medicine, th eorized that the balance of four humors (i.e. blood, yellow bile, black bile, and phlegm) forms the basis for disease, health, and personality

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20 (Berens, 2000). A balance of the warm and cool and dry and moist provi des both an ideal bodily quality and perfect persona lity qualities. Lesser qualities arise from an imbalance, or dominance, of warm and cool versus dry and moist. Thos e who are sanguine have blood dominance, cool and dry bodily qualities, and are cheerful, confident, and optimis tic. Those who are melancholy have black bile dominance, warm and dry bodily qualities, and are depressed, melancholic, or unhappy. Those who are choleric have yellow bi le dominance and are easily angered and bad tempered. Finally, those who are phlegmatic have phlegm dominance and are calm, sluggish, and unemotional. These four humors serve as th e basis for later interpretation of temperament. Immanuel Kant Immanuel Kant, an 18th century Prussian philosopher, beli eved the four humors provided a basis for temperament (Ferrar, 1994 ). According to Kant, temper ament refers to the energetic and emotional characteristics of behavior. Sanguine depicts a sociable and carefree temperament. The melancholic depicts an anxious and unhappy temperament. The choleric depicts an irritable temperament. The phl egmatic depicts a reasonable and persistent temperament. William James William James later categorized temperament into two major types: tough-minded and tender-minded (Altorf, 2005). Tough-minded temper aments are empiricists, materialistic, and sensing; tender-minded are rationali stic, idealistic, or imaginative. Wilhelm Wundt In the early 20th century, Wilhelm Wundt categorized temperaments into a quantitative two-dimensional system: energetic and emotiona l. Based on four temperament typologies, the energetic and emotional are classified as st rong or weak and changeable or unchangeable (Thayer, 1996). This theory of opposing forces in temperament was adopted by Carl Jung.

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21 Carl Jung Attitude types describe perceptional orientations that are either extroverted or introverted. Those who are extroverted focus on the immediate, objective, external world. They appear to be open, sociable, and amiable. They enjoy participa ting in social events and readily interact with others. In contrast, those who are introverted focus internally and respond subjectively to the external world. Preferring med itation and reflection to socializ ation, they tend to have a calm outward appearance and may be percei ved as closed, withdrawn, and aloof. Jung believed two basic perceptual functions rational and irrationa l, direct how we perceive information and make decisions. Ration al perceptual functiona l types are represented through the dichotomous qualities of thinking versus feeling and sensing versus intuitive. Those with a thinking functioning type are guided by reason and rely on logical approaches when drawing conclusions and making de cisions. In contrast, those w ith a feeling functioning type base decisions upon subjective and affective considerations. Irrational perceptual functioning types are ba sed upon the intensity of perception. Those with a sensing perceptual functioning type analyze information processed through the senses and are oriented to the objective, real world. In contrast, those with an intuitive perceptual functioning type rely upon the subjective, instinctual, and indirect perception of ideas. Although each individual uses all fo ur functions in their lives, th ey do so at variable levels, with variable frequency, and with variable levels of success. Jung proposed individual preferences comprise dominant f unctions, extrovert or introvert, and are supported by auxiliary perceptual functions. Based upon these typologi es, Jung proposed eigh t personality types. Table 1-2. Jungian typologies ExtrovertedSensing Extroverted-Intuition ExtrovertedThinking ExtrovertedFeeling IntrovertedSensing Introverted-Intuition IntrovertedThinking IntrovertedFeeling

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22 The joining of attitude and func tioning produces eight psychologi cal types. Those with an extroverted thinking type are logi cal, objective, and fact oriente d. Those with an extroverted feeling type are guided by feelings Those with an extroverted se nsing type are reality based and prefer tangible, perceptible things in life that appeal to the excitation of sens ation. Those with an extroverted intuitive type are insightful, percei ving the imperceptible associations of events, objects, people, and ideas. Those with an introverted thinking type tend to value their own creative ideas and theories to others. They are creative, insightful, and prefer solitude a nd self-reflection. Those with an introverted feeling type prefer ideas, moods, and intangible fee lings. They appear outwardly pleasant and sympathetic yet prefer to remain impa rtial and avoid influenci ng others. Those with an introverted sensing type juxt apose their subjective interpretati on to objects, events, or people, altering perception of objec tive reality. Those with an introve rted intuitive type are guided by the collective consciousness and are able to grasp th e inner images or essen ce of external objects. Myers-Briggs Expanding upon Jungs typologies, Isabel Myers and Kathryn Briggs (1962) believed a fourth dimension of functioning related to indi vidual preferences for lifestyle organization. Those with a judging type are or ganized, preferring to manage themselves and the external world. Those with a perceiving t ype are flexible, preferring to experience the external world. Myers and Briggs (1962) sugge st individual functioning prefer ences can be arranged in 16 distinct types based upon extroversion-introvers ion, judging-perceiving, sensing-intuitive, and thinking-feeling. Extrover sion-introversion describe attitudes and orientations. Those with an extroverted orientation derive th eir energy from the external world. They feel energized when engaged with others. Conversely, those with an introverted orientation de rive their energy from internal thoughts, feelings, and ideas and tend to reenergize through quiet solitude and reflection.

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23 The sensing-intuitive dichotomy describes a pe rception preference. Those with a sensing preference are oriented to the present. They pr efer tangible, objective information. Those with an intuitive preference are future oriented. They prefer global, abstract, and imaginative information. The thinking-feeling dichotomy describes a d ecision-making style preference. Those with a thinking preference use logic, objectivity, and anal ysis to arrive at deci sions. Conversely, those with a feeling preference use s ubjective personal standa rds, seek the mainte nance or creation of harmony, and consider the poten tial impact of decisions. The judging-perceiving dichotomy describe an environmental orientation preference. Those with a judging preference use advanced pla nning and organization. They rely upon either the thinking or feeling functions Those with a perceiving preference are adaptable and less structured. They rely upon the sensing or intuitive functions and enjoy flexibility and adaptability. Student styles questionnaire The MBTI is designed to survey the temper ament preferences of late adolescents and adults. Based upon the Jungian theory and the work of Myers and Briggs, the Student Style Questionnaire (SSQ) was selected for use in th is study because it was designed to survey the temperament preferences of children ages 6 through 17(SSQ; Oakland, Glutting, & Horton, 1996),. The SSQ incorporates four bipol ar dimensions: extrovert/introvert, practical/imaginative, thinking/feeling, and organized/flexible. The Student Styles Questionnaire (SSQ; Oakland, Glutting, & Horton, 1996), a 69 item self-report temperament scale, is designed to measure temperament preference and thus to identify learning styles preferen ces of children ages 8 through 17.

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24 The SSQ assesses four dichotomous temperamen t types: extroversion (E) /introversion (I), practical (P) /imaginative (M), thinking (T) /f eeling (F), and organize d (O) /flexible (L). Extroversion versus introversion. The extroversion and introve rsion dimensions refer to the source from which children receive energy. Students with a preference for an extroverted style generally derive energy from being with others, need consider able affirmation and encouragement from others, prefer to have many friends, and tend to take on the characteristics of those around them. They learn best through talking and cooperative group activities. An example of an item that assesses the extroversion dimension is I n school I prefer active work groups. Students with a preference for an introverted style generally deri ve their energy from themselves. They prefer to have a few close friends, have a few well-developed interests, and enjoy spending time alone. They are inclined to be hesitant to share their ideas with others. They appreciate acknowledgement of th eir careful work and reflecti on. They learn best by having time to think about and reflect upon what they have learned. An example of an item that assesses the introversion dimension is In school I prefer quiet seatwork. Practical versus imaginative. The practical and imaginative dimensions refer to perceptions of the environment. Students with a preference fo r a practical style focus their attention on what is seen, heard, or experienced through their othe r senses. Students with this preference often base their deci sions on facts and personal experi ence. They often learn best using step-by-step approaches, are provided w ith many examples and hands-on experience, and view what they are learning as applicable to their lives. They become discouraged when work seems complex. An example of an item that asse sses the practical dimension is In school I like to learn about facts that help me know lots of things.

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25 Students with a preference for an imaginative st yle prefer theories to facts and focus their attention on generalizations and gl obal concepts. They often ba se their decisions on intuitive hunches, and may overlook details when learning or doing work. They learn best when given opportunities to use their imagination and contribute their unique ideas. Th ey appreciate others who value and praise their creat ivity. An example of an item that assesses the imaginative dimension is In school I like to learn about ideas that ma ke me think in new ways. Thinking versus feeling. The thinking and feeling dimensions refer to the manner in which children make decisions. Students with a preference for a thinking style rely on objective and logical standards when making d ecisions. They want to be trea ted fairly and desire truth to be told accurately. Further, because they highly value the truth, they may tell others unpleasant things in a blunt fashion and may hurt others feel ings in the process. They may praise others infrequently and may be uncomfortable openly ex pressing their emotions or feelings. These students tend to enjoy competitive activities a nd learn best when information presented is logically organized. An example of an item that assesses the thinking dimension is I decide about things based on whats in my head. Students with a feeling style tend to rely on their feelings and own subjective standards when making decisions. They generally are comp assionate and sensitive to the feelings of others, and value harmony. Student s with a feeling style tend to learn best when engaged in cooperative activities that help personalize th eir learning. An example of an item that assesses the feeling dimension is I decide about things based on whats in my heart. Organized versus flexible. The organized and flexible dime nsions refer to the propensity of children to either make decisions promptly or delay them. Students w ho prefer an organized style like to make decisions as soon as possible and prefer stru cture and organization. They do

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26 not cope well with surprises or ch anges to their routine. They lik e to rely on lists and are likely to respond well to a more structured and organize d setting. Expectations others have for them should be communicated clearly a nd schedules clearly establishe d and followed. Students with this style like to do things the right way and en joy receiving praise for completing work in a timely manner. An example of an item that asse sses the organized dimension is I like my desk to be clean and orderly. Students who prefer a flexible style delay decision-making as l ong as possible and feel that they never have sufficient information to make d ecisions. They prefer a flexible, open schedule, enjoy surprises, and adapt well to new situations. They may not respond well to externally imposed rules and regulations. The manner in wh ich they learn best is somewhat complex. They are most highly motivated when given some fl exibility in their assignments and are able to turn work into play. However, teachers and pa rents may have to provid e structure and assist them in other ways to complete assignments on ti me. An example of an item that assesses the flexible dimension is I like my desk to be any old way. Nature versus Nurture Longitudinal and physiological studies have noted biological basis for differences in extroversion-introversion. Longitudinal and physiol ogical studies have note d biological basis for differences in extroversion-introversion. For ex ample, increased in cortical blood flow (Wilson & Languis, 1990) and levels of anterior tempor al lobe activity (Sternberg, 1990) has been associated with introversion compared to extr oversion. Joyce & Oakland (2005) posit the lower activity levels may be associated with the tendency of extroverts to seek external reinforcement from their environment and the need for introve rts to withdraw from others in order to

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27 rejuvenate. Other differences not ed between introverts and extrove rts include increased risks for hypertension and heart disease for ex troverted individuals (Shelton, 1996). Biological differences were also noted in those with practical versus imaginative temperaments. Greater left hemispheric activ ity has been associated with the practical temperament. Conversely, greater right hemis pheric activity has been associated with the imaginative temperament (Newman, 1985). Increased risk of heart disease and hypertension also were associated with practical temperament (Shelton, 1996). In addition to biological differe nces, differences in the enviro nment also are noted. Some of the factors influencing temperament styles are purported to include family values (Rowe & Plomin, 1981), family size (Rosenkrantz, V ogel, Bee, Broverman, & Broverman, 1968), dissonance between environment and temperament styles, perceived self competence (Myers & McCaulley, 1985), gender, race, and ethnicity (Myers, & McCaulley, 1985, Whiting & Whiting, 1975), intelligence (Levy, Murphy, & Carlson, 1972), and stages of development (Bassett, 2004; Thayer, 1996). Cognitive and psychosocial development may strengthen or shift learning styles preferences. Cultural and development trends in the type a nd strength of learning style preferences were identified using the SSQ (Bassett, 2004; Th ayer, 1996; Oakland, Alghorani, & Lee, 2007; Oakland, & Lub, 2006).. The preference for extrav erted learning style increases from age 8 to 13, then plateaus (Bassett, 2004). Young children tend to prefer thinking styles, with the preference for feeling styles in creasing during the late teen age years. Younger children demonstrate a higher preference for organized styles while older ch ildren prefer more flexible styles (Bassett, 2004; Thayer, 1996). Young childre n and older teenagers are most likely to prefer imaginative st yles (Bassett, 2004).

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28 Gender and ethnicity also impact childrens te mperament. Males gene rally prefer thinking, flexible, and practical styles. In contrast, females generally prefer feeling, organized, and imaginative styles (Bassett, 2004 ; Thayer, 1996). Preference for thinking styles is relatively stable for males. However, preference for feelin g styles increases with age for females (Bassett, 2004). Although both young males and females exhibi t a strong preference for organized styles, a growing preference for flexible styles is seen in older male s. However, a preference for organized style remains relatively stable in fema les. Gender differences for practical versus imaginative style were evident a nd inconsistent (Bassett, 2004). Compared to Caucasian children, African American children demonstrate a higher preference for a thinking style than a feeling st yle. Compared to Caucasian children, African American and Hispanic childre n demonstrate a higher preferen ce for practical and organized styles than for flexible and imaginat ive styles (Stafford, 1994; Thayer, 1996). Temperament based learning styles differences were noted on graduati on rates, levels of achievement, perceptions of teachers, academic pe rsistence, and prevalence of gifted aptitude (Schurr et.al., 1997; Myers et. al., 1998; Cornett, 1983; Oakland et. al., 2000). Learning style preferences indicate how student s gather and process information within their environment. Because classroom practic es reflect school policy, accepted pedagogy, and teacher preferences for instructional met hods, students may perceive their classroom environment as either congruent or incongruent with their prefe rred learning styles. This, in turn, may affect their perceived level of motivation to engage. Motivation Motivation refers to the incentive for goal di rected behavior. Academic achievement behavior refers to the demonstr ation of ability or competency in academic settings ( Maehr & Nicholls ,1980). Academic achievement motiv ation refers to the incentive to improve

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29 educational performance, demonstrate educationa l mastery, or engage in educational tasks. Academic achievement theories of motivation are disposition based or situation-specific. (Brophy, 2001). Disposition based theories assume motiv ation is innate, universal, stable, and generalizeable (Brophy, 2001; Deci, 1991). Derived from personal be liefs and values (Epstein, 1994), motivation is tied to emotions, cognitions, and socialization. The affective qualities of motivation (McClelland, 1987) that result from task engagement and completion (Ford, 1992), include past experiences (McClelland, Kestner, & Weinberger, 1989), affective outcomes (e.g. the need for esteem) (Dweck & Leggett, 1988 ), autonomy (Harter & Connel, 1984; Deci, & Grolnick, 1995), competency, and mastery (Har ter & Connel, 1984; Deci & Ryan, 2002) appear early in a childs development. Individual values and expecta tions (Eccles, 1983), attributions (Weiner, 1986), self-regulation, and self-efficacy beliefs (Hagen & Weinstein, 1995; House, 2002; Pintrich, & De Groot, 1990; Schunk, 1981; Urdan & Maeher, 1995) are important determinants of academic achievement motivation levels. Motivation for learning is developed through socialization within the home and school (Brophy, 2001). Situation-specific theories of motivation a ssume extrinsic factors within the learning environment (Ames, 1992; Freeman, 2004), includi ng outcome expectancies, prior successes and failures (Ames & Archer, 1987), perceived va lue for the outcome (Logon, 1968), and sociocultural contexts of goa l attainment (Maehr & Nicholls, 1980; Nicholls, 1992), interact with the specific goals to elicit motivation for achieveme nt behavior. External rewards (Madden, 1997), concrete feedback (Bardwell, 1984), the reasonabl eness of goals (Hart, 1989), level of task difficulty (Wigfield, 1994), performance versus mastery (Ames, 1992; Elliott & McGregor, 2001; Pintrich, 2000), and demonstration of co mpetency (Dweck & Elliott, 1983; Dweck &

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30 Leggett, 1991) are considered to be important determinants of le vels of motivation for academic achievement behavior. Gender and racial differences may influence le vels of academic motivation. For example, in a study of middle school student s, girls reported higher levels of motivation compared to boys, seventh graders reported higher levels of amotivation compar ed to eight graders, and African American students reported highe r levels of amotivation than White students (Attaway, 2004). Additionally, students who experien ce racial discrimination may exhibit declines in achievement and perceived importance of tasks. However, students with strong ethni c support demonstrated increased academic motivation when faced with adversarial climates (Eccles, Wong, & Peck, 2006). Thus, academic achievement motivation, the dr ive to put forth persistent effort for educational achievement, is influenced by the inte rnal qualities of the student and the external qualities of the learning environment (Ames, 1992; Davis, 2004; Davis, Davis, & Smith, 2004; Ford, 1992; Heider, 1958). Theories of academ ic achievement motivation relative to these internal and external qualit ies are reviewed below. Expectancy-Value Theory In expectancyvalue theory, pr ior experiences are thought to play an important role regarding students motivation (Atkinson, 1980). Motivation for future task engagement (Wigfield, 1994) and expectations for future su ccess (Jacobson, 2000) are de veloped at an early age (Wigfield, Eccles, & Rodriguez, 1994). Those who experienced failure, compared to those who experienced success, are likely to partic ipate less, report lower levels of motivation (Jacobson, 2000), and are more likely to demonstr ate academic avoidance, resulting in lower academic achievement (Eccles, Wigfield, Midgel y, Reuman, MacIver, & Feldlaufer, 1983). S tudent motivation for engagement is influenced by the cost of engaging in a task, the perceived

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31 usefulness of the task, and self-e fficacy beliefs (Eccles, 1983). Ma stery of subject material is enhanced through positive attitudes for learni ng and valuing effort (Ames & Archer, 1987). Attribution Theory Students attributions for academic success or failure are thought to be important components of motivation (Weiner, 1986). Internal attr ibutions for success and failure refer to the internal assignment of res ponsibility for outcomes based upon co mpetency beliefs. Internal attributions for ability can be vi ewed as fixed or malleable. Co mpared to students who attribute failure to innate deficiencies, t hose who attribute failure to poor effort are more likely to persist with difficult tasks (Ames, 1983; Weiner & Kukl a, 1970). Thus, attributions for performance may be associated with level of effort, which is changeable, or due to innate ability, which may be considered fixed. External attributions assign outcome respons ibility to uncontrollabl e factors within the environment. For example, if a student receives high marks on an assignment, the student attributes this to task ease. If a student receives low mark s on an assignment, the student attributes this to task difficulty. Rather than recognizing the importance of effort and ability, the student perceived that task quali ties determine his or her success. Compared to students with external attributions, those with internal attri butions for success are more likely to engage in tasks that are challenging (Ame s, 1983; Weiner & Kukla, 1970). Based upon attribution theory, academic goals are thought to be task or ego involved. Task involvement refers to the inherent valu e of learning and where success and failure are attributed to effort. Attributi on theory highlights the focus on the learning task and the strategies required for mastery (Weiner & Kukla, 1970). Ego-involvement tasks refer to students intern al attributions for success and failure. The focus is on the self. Learning is perceives as a means to avoid appearing deficient.

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32 Self-determination Theory Motivation is influenced by self-esteem, (Dweck & Leggett, 1988), self-efficacy, (Anderson, 2002), and self-determination (Ryan & Deci, 2000; Eccles, 1 983). Selfesteem refers to feelings of self worth (Dweck & Legge tt, 1988). Self-efficacy refers to ones beliefs of competency and capability (Anderson, 2002). Self -determination refers ones sense of autonomy (Ryan & Deci, 2000). Individual differences fo r approaching success or avoiding failure are associated with self-efficacy beliefs about the pl ausibility of goal attainment and affective outcomes associated with success or failure (Ford, 1992). A sens e of individuality, superiority, and self-determination serve to motivate purposeful behavior. Self-determination theory of motivation assert s the need to feel related, competent, and autonomous promotes intrinsic motivation (Ryan & Deci, 2000). Thus, students are more likely to engage in academic tasks when they feel a sense of affiliation within the classroom, of personal initiative, and that they have the knowle dge and skills needed to achieve academically (Deci & Ryan, 2002; Kakman, 2004; Turner et.al., 1989). Goal Achievement Theory Goal achievement behavior is thought to be either mastery or performance based (Ames, 1992; Elliott & McGregor, 2001; Pint rich, 2000). Mastery goals are thought to involve intrinsic learning goals and competency development. Performance goals are thought to focus on the demonstration of ones ability (Dweck & Elliot t, 1983; Dweck & Leggett, 1991). The nature of the task provides the directive for either ma stery or mastery performance approached. For example, while the demonstration of readi ng comprehension may be mastery based, the recitation of multiplication tables may be performance based.

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33 Goal Orientation Theory Compared to theories that are disposition base d, goal orientation is thought to be situationspecific (Nicholls, 1992), or base d upon the specific characteristi cs of the task. Situationspecific theories are a ssociated with personal characteristi cs (Dweck, 1999), the nature of the environment (Ames, 1992), the socio-cultural context (Maehr & Nicholls, 1980), and social status variables. Three orientations to goal achievement are based upon ones desire to demonstrate high ability, to avoid the demonstrat ion of low ability, or to develop competence (Kaplan, 1992). According to this theory, the orientation dete rmines how learners are guided in their activities, thoughts, and feelings. For example, mastery goals focus on promo ting understanding, developing competence, and improvement. Mastery oriented learners beli eve ability is malleable and based upon effort. These learners tend to focus on competency and skill acquisition, are more likely to use cognitive strategies, and are more likely to seek assist ance when faced with difficult tasks (Dweck & Legget, 1988) Perceptions of high self-efficacy are mo re likely to result in performance approach. In contrast, perceptions of low self-e fficacy are more likely to result in performance avoidance (Kaplan, 2002). If the learner believes ability is fixed, static, and unalte rable, their orientation is performance based. Performance-avoidance goa ls involve a desire to avoid appearing incompetent or less competent. The learner is more likely to lack strate gies when faced with a difficult task and may exhibit patterns of l earned helplessness when faced with failure. Performance-approach goals involve a desire to demonstrate competence. Learners seek to gain positive judgments about their competency, tend to avoid challenging situations, and focus on grades as a measure of performance.

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34 The Motivated Strategies for Learning Qu estionnaire (Pintrich & DeGroot, 1990) was designed to measure self-efficacy and intrinsi c motivation. Based upon the expectancy-value theory, MSLQ uses a 5-point Likert scale to asse ss students perceptions about their expectations based upon their performance, ability, task values and task utility. The MSLQ has been found to predict achievement in middle school mathematic s (Davis, 2004; Davis, Davis, & Smith, 2004). Although the expectancyvalue theory is releva nt to measures of academic motivation, it does not account for an individual s orientation towards specific acad emic tasks. The Patterns of Adaptive Learning Survey (PALS; Midgley, et al., 1996) was designed to measure mastery performance, performance avoidance, and perf ormance approach goal. Mastery performance orientation refers to the extent to which st udents engage in academic tasks to promote competence. An example of items reflecting the mastery performance is Its important to me that I learn a lot of new concepts this year. Performance avoidance orientation refers to the extent to which students desire to avoid demonstrating incomp etence. An example of items reflecting performance avoidance is Its important to me that I dont look stupid in math class. Performance approach orientation refers to th e extent to which stude nts are interested in demonstrating competence. An example of ite ms reflecting performance approach is Its important to me that other students in my ma th class think I am good at my class work. The PALS was selected based upon the th eoretical underpinnings that academic achievement motivation is based upon the individual s perception of orientation toward the task itself, rather than external motivational factors. This measure appeared to be consistent with other measures selected in that the interest in individual perceptions of the environment are considered. Additionally, the eff ects of gender and race /ethnicity will be examined. However,

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35 since this study examines students self-reported levels of motivation to their rate of achievement gain, significant differe nces are not expected. Aptitude and Achievement Aptitude. Aptitude, ones optimal ability for cognition, is generally measured through the use of intelligence scales. The Wechsler Intelligence S cale for ChildrenFourth Edition is one widely used measure of intelligence. The WISC-IV is comprised of 15 subtests measuring verbal comprehension, perceptual reasoning, processi ng speed, working memory, and full scale IQ. The WISC-IV is designed for children ages 6 to 16. Administered in dividually, the 60-80 minute administration time suggested by Wechsler (2003) was found to be underestimated in a study by Ryan, Glass, and Brown (2007). Thus th e administration time precludes its use from this study. Additionally, though measures of inte lligence are designed to capture aptitude, the role of prior experience, such as quality and level of education, English proficiency, and level of vocabulary proficiency may depress IQ scores for individuals with minority status (ShuttleworthEdwards, Kemp, Rust, Hartman, & Radloff, 2004). For example on the Wechsler Adult Intelligence Scales-Revised and Wechsler Inte lligence Scales for Children-Third Edition, differences by ethnicity were noted on the vocabulary subtests (Kaufman, McClean, & Reynolds, 1998; Paolo, Ward, Ryan, & Hilmer, 1996; Ardilo & Mareno, 2001). Additionally, block design, a nonverbal subtest assessing perc eptual reasoning abilities also reported significant differences by race/ethnicity, unless educat ional experiences were contro lled for (Kaufman, McClean, & Reynolds, 1998; Overall & Levin, 1978; Paolo, Ward, Ryan, & H ilmer, 1996). When level and qualityof education are controlled for, results from IQ tests te nd to be more congruent between ethnicities (ShuttleworthEdwards, Ke mp, Rust, Hartman, & Radloff, 2004).

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36 According Hayes (1999), a strengt h of the MAT-SF is the lack of systematic bias against gender or ethnic group. Thus, th e MAT-SF was selected for its ease of administration, short amount of time required for task completion, and consistency across gender and race/ethnicity. The MAT-SF percentile ranks served as the controlled variable for aptitude. Academic aptitude may impact rate of achievement gains. The Matrix Analogies Testshort-form (MAT-SF; Naglieri, 1985) provides a relatively quick screening of ones aptitude based upon four factors: reasoni ng by analogy, serial reasoning, pattern completion, and spatial visualization. The MAT-SF provides a measure of visual conceptual ab ility, highly correlated with cognitive processe s involved in algebra. Achievement Achievement is measured as a gain in know ledge and skills. This study proposes and examination of achievement in mathematics ove r the course of an academic semester in secondary school algebra I classes. Research has demonstrated a gender trend, where boys outperform girls in math in middle school a nd beyond (Chatterji, 2004; Chatterji,2005; Goh, & Fraser, 1995; Spelke, 2005. However, developmenta l studies indicate sim ilarity between males and females in achieving mathematical milestone s. For example, no gender differences were noted in the acquisition of pro cesses that involve recognizing geometric shapes, angles and distance (Spelke, 2005), identifying landmarks in a visual-spatial relations hip (Hespos & Rochat, 1997; Acredolo, 1978; Gouteux & Spel ke; Riser, 1979), the ability to mentally rotate of objects (Hespos & Rochat, 1997), orientation to geometri cal objects (Herner & Sp elke, 1994; Hespos & Rochat, 1997; Learmouth, Nadel, & Newcombe 1999), understanding number word meanings (Griffin & Case, 1996), and the development of spatial language (Herne r & Spelke, 1994). While no gender differences have been associat ed with the development and acquisition of primary mathematical abilities (A credolo, 1978; Gouteux & Spelke, 2001; Riser, 1979; Herner &

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37 Spelke, 1994; Hespos & Rochat, 1997; Lear mouth, Nadel, & Newcombe, 1999; Spelke, 2005), other studies suggest gender differences emerge and the math achievement gap widens during the development of more complex processes asso ciated with higher leve l quantitative reasoning (Beilstein & Wilson, 2000). In tasks involving arithmetic calculation and remembering the spatial location of objects, fema les outperform males. However, on tasks involving solving word problem and remembering the geometric arrange ment of the environment, males outperform females (Halpern, 2000; Hyde, 2005). Differences in the use of strategies for solv ing mathematical tasks also factor in the performance level. Males tend to use the sp atial relationships for object comparison while females focus on object features (Voyer, Voyer, & Bryden, 1995). Males continue to use spatial imagery rather than verbal computation when solving word problems (Geary, Saults, Liu, & Hoard, 2000). The differences in strategies impact performance on standardized tests, such as the SAT-M, with males outperforming females (Gallagher & Kaufman, 2005). Race/ethnicity has also been found to factor si gnificantly in measures of math achievement in standardized tests, such as FCAT performa nce. In 2004, FCAT statistics for math indicated 37% more Whites achieved a level 3 than did Af rican American students and 20% more White students achieved level 3 than did Hispanic stude nts. Trends indicate all ethnicities are improving in FCAT performance. However, c onsistent progress gains across race/ethnicity, maintains the racial/ethnic achievement gap. Th us, although trends indicate the achievement gaps have been closing in elementary grades the racial/ethnic achievement gap observed in middle and high school have not changed significantly since 2000 (Chatterji, 2004; Chatterji,2005).

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38 Gender and race/ethnicity have b een demonstrated to factor in achievement levels in math (Chatterji, 2004; Chatte rji, 2005; Lee, 2002). However, in this study, achievement compares individual performance on the sample FCAT test to their previous performance on the same test administered at an earlier time. Thus, wh ile comparisons across gender and ethnicity are examined, they are not expected to significantly affect the results. The FCAT sample test was selected in order to provide a standard sample of mathematical abilities across teachers and classrooms. Proposal This proposed study will be conducted in thr ee stages. The purpose of study 1 is to analyze the effect of gender a nd race/ethnicity on achievement. Additionally, study one purports to narrow the focus of variables that impact math achievement in order to increase the efficiency and effectiveness of later work. The purpose of st udy 2 is to more directly test the dissertations main hypotheses. Hypotheses Students respond to their learning environment, in part, as a result of personal, family, teacher, and peer influences (Brophy, 2001; Deci, 1991). The manner in which students perceive, approach, and respond to their learning environments is determined, in part, by their learning style preferences (B argar, & Hoover, 1984; Lawren ce,1982; Oakland et.al. 1996; Thayer, 1996;Thomas & Chess, 1968). Motiva tion and perceptions of classroom climate influence achievement. (Davis, 2004; Davis, Davis, & Smith-Bonahue, 2004; Townsend & Hicks, 1997; Urdan & Maeher, 1995). Classroom climate is measured by seven variab les, preferred learning styles by four, and motivation by three. Inasmuch as there are f ourteen independent variables, an attempt to decrease their number would be advantageous to conducting subsequent st atistical analyses.

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39 Thus, the goal of study one is to determine re lationships between classroom climate and math achievement in order to identify classroom clim ate qualities that have the strongest impact on math achievement. Figure 1-1. Relationship of constr ucts examined in this study Study One Study one is designed to investigate the followi ng question: what are the relationships between each of the seven domains of classroom climate and math achievement (Figure 2-2)? Motivation Classroom Climate Perceptions Achievement Temperament Aptitude

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40 Figure 1-2. Relationships between classroom climate domains with math achievement. Figure 1-3. Contribution of classr oom climate, motivation, and pref erred learning styles to math achievement. The following hypotheses are based upon the assu mption that approximately three of the seven classroom climate domains from the WIHI C will demonstrate a positive relationship with math achievement (Dorman, 2003; 2004). Therefore, they will be referred to as variable 1, Equity Cooperation Task Orientation Investigation Involvement Teacher Support Student Cohesiveness Math Achievement Math achievement Motivation Classroom climate Preferred learning styles

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41 variable 2, and variable number 3 at this stage in the study. M odifications in the hypotheses may be made based upon actual knowledge of the rela tionships between seven classroom climate domains and math achievement. The study tests the assumption that classroom climate (Davis, 2004; Davis, Davis, & Smith-Bonahue, 2004; Goh & Fraser, 1998), motiva tion (Davis, 2004; Davis, Davis, & SmithBonahue, 2004), and preferred lear ning styles each contribute signi ficantly to math achievement (Figure 2-2). The study also test s the assumption that these thr ee variables, in confluence, contribute to math achievement beyond that obtained when each of the th ree variables is used individually to predict math achievement (Figure 2-3). Thus, this study examines relationships betw een classroom climate and math achievement, by testing the following hypotheses: Three classroom climate variables will dem onstrate a positive relationship with math achievement, controlling for academic aptit ude, gender, grade level, and ethnicity. This study also examines relationships betw een motivation and achie vement by testing the following hypotheses: Mastery domain scores will demonstrate a positive relationship with math achievement, controlling for academic aptitude, gender, grade level, and ethnicity. Performance avoidance scores will demons trate a negative relationship with math achievement, controlling for academic aptit ude, gender, grade level, and ethnicity. Performance approach scores will demonstrate a positive relationship with math achievement, controlling for academic aptit ude, gender, grade level, and ethnicity. Study Two This study examines the relationships betw een learning styles pr eferences and math achievement and examines the impact of the c onfluence of classroom climate, motivation, and preferred learning styles on math achieve ment by exploring the following questions:

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42 What is the relationship between extroverted -introverted learning style preferences and math achievement, controlling for academic aptitude, gender, grade level, and ethnicity? What is the relationship between practical-ima ginative learning style preferences and math achievement, controlling for academic aptit ude, gender, grade level, and ethnicity? What is the relationship betw een thinking-feeling learning style preferences and math achievement, controlling for academic aptit ude, gender, grade level, and ethnicity? What is the relationship between organized-f lexible learning style preferences and math achievement, controlling for academic aptit ude, gender, grade level, and ethnicity? Will the combination of knowledge of cla ssroom climate, motivation, and preferred learning styles will contribute to math achie vement beyond that obtained when these three variables are used individually to predict math achievement, controlling for academic aptitude, gender, grade level, and ethnicity (Figure 2-3)?

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43 CHAPTER 2 MATERIALS AND METHODS Participants One hundred three participants were selected from 150 ninth and tenth grade high school mathematics students enrolled in an Algebra I cl ass in a mid-size city public school in North Central Florida. The high school population of 1895 students within the school district includes Asians (3%), Hispanics (6%), African Americans (34%), and Caucasians (56%), of whom 48% are male. The average teacher-student ratio report edly is 1:22. Thirty-two percent of students within the school are e ligible for free or reduced lunch. The school graduation rate is 75%. The participants included 81 ni nth and 22 tenth grade math st udents enrolled in one of 5 Algebra I classes offered. One female teacher ta ught three algebra I classes, of which 2 were morning classes and one was an afternoon class. On e male teacher taught tw o afternoon classes. The final sample included 49 males and 54 fema les of which 48.5% were Caucasian, 12.6% were African American,10.7% were Hispanic, and 2. 9% were Pacific Islander-Asian, 5.8% were multiracial, and 19% declined to identify themselv es according to race/ethnicity. The income ranged from below $20,000 per annum to above $65, 000 per annum. Of these, 22% declined to respond, 27.2% believed the household income to range between $20,000 and $45, 000 annually, 25.2% believed the household income to ra nge above $65,000 annually, 20.4% believed the household income to range between $45,000 and $65,000 annually, and 5.8% believed the household income to range below $20,000 annually. Table 2-1. Participant demographic information Race/Ethnicity Frequency Percent Caucasian 50 48.5 African American 13 12.6 Asian/Pacific Islander 3 2.9 Hispanic 11 10.7 Multiracial 6 5.8

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44 Table 2-1. Continued Race/Ethnicity Frequency Percent South African 1 .9 Frequency Percent Below 20k 6 5.8 20k-45k 28 27.2 45k-65k 21 20.4 Above 65k 26 25.2 No response 22 21.3 Grade Level Frequency Percent 9th Grade Students 81 78.6 10th Grade Students 22 21.4 Table 2-1 Gender Frequency Percent Female 54 52.4 Male 49 47.6 Mothers Education Level High school 18 16.5 Some College 25 22.9 College Degree 31 28.4 Masters Degree or Above 29 26.6 Fathers Education Level High school 27 26.2 Some College 19 18.4 College Degree 22 21.4 Masters Degree or Above 35 33.9 Instrumentation Classroom Climate The What Is Happening In This Class? (WIHIC ) questionnaire was used to acquire data on seven domains of classroom climate designed to predict learning outcome s (Fraser, Fisher, & McRobbie, 1996). The WIHIC assesses the followi ng domains: student cohesiveness, teacher support, student involvement, investigation, task orientation, cooperation, and equity (Fraser, Fisher, & McRobbie, 1996; Aldridge & Fraser, 1997). Internal consistency estimates using Cronb ach coefficient alpha are .81 for student cohesiveness, .88 for teacher support, .84 for i nvolvement, .88 for investigation, .88 for task orientation, .89 for cooperation, and .93 for equi ty (Aldridge & Fraser 2000). Discriminant

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45 validity coefficients of the seven domains of classroom climate ranged from .32 for student cohesiveness to .49 for involvement in a cr oss-national study of Au stralian and Taiwanese students (Aldrige, Fraser, & Huang, 1999). For faci litation in anal ysis, studies often reduce the number of variables on the WIHIC to measure classroom climate (Allen & Fraser, 2002; Hunus & Fraser, 1997; Khine & Fisher, 2001; Khoo & Fras er, 1997). For this st udy, three out of seven variables will be selected based upon the st rength of their correlation to achievement. Learning Style Preferences The Student Styles Questionnaire (SSQ; Oakland, Glutting, & Horton, 1996), a 69 item self-report temperament scale, is designed to measure temperament preference and thus to identify learning styles preferences of child ren ages 8 through 17. The SSQ assesses four dichotomous temperament types: extroversion (E ) /introversion (I), prac tical (P) /imaginative (M), thinking (T) /feeling (F ), and organized (O) /flexibl e (L). It was normed and standardization on 7,609 students ranging in ages from 8 through 17, including 5547 Anglo American, 1194 African American, and 868 Hispanic students. Students were selected from 61 school districts in 29 states plus Puer to Rico (Oakland, Glutting, & Horton, 1996). Test-retest reliability coefficients derive d over a 9 month period, ranged from .67 on the practicalimaginative dimensions to .80 on the extroversionintroversion dimensions, with an average test-retest reliability coefficient of .74. Studies indicate good convergent validity with the Myers Briggs Temperament Inventory, good divergent validity with achievement and intelligence, and good stability for persons who differ by age, gender, and race/ethnicity (Oakland, Gutting, & Horton, 1996; Oakland, Glutti ng, and Stafford, 1996; Stafford & Oakland, 1996).

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46 Motivation The Patterns of Adaptive Learning Survey (P ALS; Midgley, et al., 1996) was designed to measure mastery performance, performance avoida nce, and performance approach goal. Other qualities include measures of academic self-e fficacy and self-handicapping strategies. The PALS consists of 56 items and uses a 5 point Likert response scale. Internal consistency estimates are reported to be .86 for mast ery performance scales, 75 for performance avoidance scales, and .86 for performance approach scales (Midgely, 2002). Studies indicate good convergent validity with other measures of motivation, good construct validity, and good stability for PALS (Lipman & Moore, 2005; Anderman, Urdan, & Roeser, 2003). Academic Aptitude Academic aptitude was measured using the Ma trix Analogies Test Short-Form (MAT-SF; Naglieri, 1985). The MAT-SF is a 34 item group administered test of non-verbal reasoning ability. The MAT-SF provides a relatively quick screening of ones apt itude based upon four factors: reasoning by analogy, seri al reasoning, pattern completion, and spatial visualization. The MAT-SF was normed and standardized on 4 ,468 students, grades kindergarten through 12, representative of the 1980 U.S. Census for age, gender, ethnicit y, geographic region, and community size. Internal consistency estimates range from .63 to .89. Test-retest reliab ility estimates range from .51 to .91. The MAT-SF demonstrates good convergent validity with other measures of intelligence, such as the Wechsler Intelligence Scale for Childrenrevised (Karnes & McGinnis; 1994Slate, Graham, & Bower, 1996), Wechsle rThird Edition (WISC-III; Wechsler, 1991)(Hayes, 1999; Prewett,1995), the Stanford Bi netFourth Edition (Prewett, & Farhney, 1994), the Kaufman Brief Intelligence Test (H ayes, 1999; Prewett, 1995; Slate, Graham, &

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47 Bower, 1996) and the Kaufman Test of Edu cational Achievement-Brief Form (Prewett & Farhney, 1994). Achievement Achievement was measured as the change in values between scores on a sample FCAT test administered at the beginning of the school year ( pretest data, August, 2006) and then readministered at the midpoint of the year ( posttest data, January, 2007). The FCAT sample included two different tests, one for the ninth grade students and one for the tenth grade students based upon a representative sample of math skills required for the expected level of mastery for students in their correspondi ng grade. Raw scores for the ninth grade were converted to z-scores in order to better represent th e variance in population and to st andardize comparisons across the test versions. Raw scores were then converted to z-scores for the tenth grade test. Changes in achievement were determined by subtracting the i ndividual pretest z-scores from their posttest zscores. Procedure Approval for this proposed study was sought th rough the Institutional Review Board (IRB) at the University of Florida (UF). The I RB was established in accordance with Federal Regulations (45 CFR 46 and 21 CF R 56) and reviews research involving human subjects under the UF Federal Wide Assurance under the regula tions promulgated by the U.S. Department of Health and Human Services designed to safeguard the rights and welfare of human subjects. A copy of the research proposal and all questionnai res, surveys, and instruments was submitted for review and approval. Parental consent for survey participation was obtained for initial recruitment of each student (Appendix A.3). A consen t form describing the nature of the school-wide project, the purpose of the study and the types of questions to be asked, as well as the task requirements and

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48 time frame involved was composed then approve d through the Institutional Review Board (IRB) at the University of Florida (UF). Consent to participate was obtained from the parents or guardians of the students. One week before the study began, 150 parent consent forms were delivered to the participating math teachers to dist ribute to their students. Students were asked to deliver the form to their parent to solicit consent. Parents were asked to sign the consent form to indicate their agreement to allow their child to participate and re turn the form, with their child the following day. Students returned their signe d consent forms to their teacher. The consent forms were collected from the teacher three da ys before the study and the day of the study. Students who misplaced their forms were re-issued forms to take home to their parents. The teachers reminded the students each day prior to data collection to return their consent forms. Assent to participate was obtained from the st udents. An assent form was composed then approved through the IRB (Appendix A.4). Students we re given the assent form the first day of the data collection. The study was orally desc ribed to the students, the assent form was explained, and then students were asked to si gn the form if they wi shed to participate. Agreement to participate also was obtained from the principal and teachers during the initial meeting to discuss the st udy and arrange for the testing to take pla ce Students lacking consent or assent were excluded from the study. Students were asked to comple te an F-CAT sample pre-test during class in August, 2006 consistent with their grade level. The tests we re administered by the classroom teachers. The tests were scored according the corresponding answ er booklet given to the teacher. For three classes, the tests were scored by the researcher. For two classe s, the tests were scored by the classroom teacher. All posttests were scored by the researcher. One problem associated with geometry was eliminated from the tenth grade pr eand post-test. This allowed consistency in

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49 amount of points scored across grades. The raw scores were convert ed to z-scores for consistency in comparisons across grades, as well as to better represent th e amount of variance in a normally distributed population. Students were asked to complete the SSQ, MAT-SF, WIHIC, PALS in December, 2006. Each student was given a data packet consisting of the assent form, a demographic survey, and a copy of each instrument. The demographic su rvey consisted of questions related to race/ethnicity, estimated economic status of stude nts families, including income and education level of parents, perceived va lue for math, overall math grad e achieved the prior year, and expectations for assigned midterm grade in math in the current academic year. Students were asked to complete the survey as part of their survey packet. St udents were advised they were not required to complete any questions or items durin g the survey process if they did not wish to disclose the information. Each instrument, including the demographic su rvey data, was orally explained using a copy of each as a visual aid. Students were allowed to complete the instruments in any order they wished, with the exception of the aptitude test, which was administered in groups of ten students with a twenty minute time limit. During each data collection day, the researcher would review the requirements and was available to the class for questions and comments. When students were finished, they raised their hand. The researcher reviewed their surv eys for incomplete or double scored items. Students were asked to correct those items. Once completed, the surveys were returned to the packet envelope and collected and marked acco rding to grade level, teacher, and class period. Students were reminded prior to each data collecti on that they could discontinue participation if

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50 they wished, with no penalty. Students with incomplete data packets were later eliminated from the study. Students were asked to complete the math post-test in January, 2007. The same F-CAT sample test was re-administered consistent with the first administration. Each teacher explained the test and gave directions on completion. The teachers and the research er were available for questions and comments during the administration. Students were given calculators consistent with the directions of the sample F-CAT admi nistration. Students raised their hand when finished. The tests were collected by the resear cher and categorized by class period, teacher, and grade level. The tests were scored consistent with the first administration, and the raw scores were converted to z-scores. Math achievement was determined by subtracting the post-test z score from the pretest z score. Analysis: Data from the What is Ha ppening in My Classroom Scale ( WIHIC ; Fraser, Fisher, & McRobbie, 1996) the Patterns of Adaptive Learning Scales (PALS; Midgley, et al., 1996), and Student Styles Questionnaire (SSQ; Oakland, Glutting, & Horton, 1996 ) were entered into a text file and imported to SPSS (15.0 for Wi ndows). An integrity check was performed to ensure the accuracy of the data. Data files were sorted by descending order using the student identification number with the order of variables lis ted in the same sort sequence. Initially, data for climate, motivation, and grades were entere d into imputed files. Data were merged by identification number per each domain, matching cases listwise. Using frequency and descriptive statistics, composite data were screened for missing values, normality, and outliers. A composite sc ore was computed using the WIHIC domains of equity, student cohesiveness, te acher support, involvement, inve stigation, task orientation, and cooperation. A composite score was computed us ing three PALS domains of mastery approach,

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51 performance approach, and performance avoidan ce. Scores were reported on a continuum of positive and negative values for the SSQ qualiti es of extroversion-introversion, practicalimaginative, thinking-feeli ng, and organized-flexible. A preliminary analysis, using an independe nt t-tests and one-way ANOVA, was conducted to check for differences in gender, race/ethnicity grade level, class period, and teacher. The three independent variables (i.e classroom climate, motivation, and preferred learning styles) were examined in relation to the dependent vari able of math achievement after controlling for aptitude. Tests for gender, grade, and race /ethnicity, were conducted. Significance was determined using p < .05. For study one, a covariate analysis provided a m easure of the strength of the relationships between all seven domains of classroom clim ate with math achievement, controlling for academic aptitude. The three domains that correlat ed highest with achievement were selected as measures of classroom climate and designated as involvement, cooperation, and task orientation. Study two utilized data on motivation and lear ning styles. Motivation was assessed by mastery performance, performance avoidance, an d performance approach. Learning styles were reflected in data on extrovers ion-introversion, practical-imagi native, thinking-feeling, and organized-flexible. Math achievement was asse ssed by differences between standardized preand post-test scores. Academic aptitude was assessed by the Matrix Analogies Test Short Form. The hypotheses were tested initially for possible gender and grade level differences. Some gender differences were found. Thus, gende r was included as a controlled variable. Three multiple-regression models were used to examine the impact of classroom climate on achievement, motivation on achievement, and learning styles on achievement. The final analyses examined the linear relationship between the predictive variables of classroom climate,

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52 motivation, learning style preferences, grade le vel, gender, and aptitude on achievement. Nonsignificant interactions were dropped, one at a time, from the regression equation. The proportion of explained variation in math achievement by the pred ictive variables in confluence was examined.

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53 CHAPTER 3 RESULTS Preliminary Analysis of Data Gender Effects An independent t-test was conduc ted to test for gender differen ces in ratings of classroom climate, motivation, learning styles preferences, aptitude, and achievement (Table 3.1). Gender differences were found on student cohesiveness, (p < .03) and teacher support (p < .03). Compared to males, females reported higher ratings for student cohesiveness and teacher support. Gender differences were also found on mastery motivation (p < .03). Compared to males, females reported higher ratings for master y performance. Gender differences also were found on extroversion (p < .03). Compared to fe males, males were more likely to express a preference for extroversion. Gender differences were found on academic aptitude. Compared to females, males displayed higher academic aptitude. Grade Level Effects An independent t-test was conducted to test for grade level differences in ratings of classroom climate, motivation, learning styles pr eferences, aptitude, and achievement (Table 32). No significant grade level differe nces were found on these variables. Table 3-1. Gender differences Gender MSDM Differencet p-value What Is Happening In my Class (composite score) Student Cohesiveness male 31.815.98-2.49-2.21*.03 female 34.294.50 Teacher Support male 27.197.17-3.20-2.19*.03 female 30.396.78 Cooperation male 26.947.88-3.01-1.85.07 female 29.957.64 Equity male 33.716.52.00.00.10 female 33.719.22 Involvement male 24.097.74-2.37-1.55.13 female 26.466.74 Task Orientation male 33.006.39-1.59-1.22.23 female 34.586.01

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54 Table 3-1. Continued What Is Happening In my Class (composite score) Gender MSDM Differencet p-value Investigation male 24.027.67.44.26.80 female 23.598.63 Student Styles Questionnaire (T score) Extroversion male 22.4859.71-26.68-2.17*.03 female 49.1641.41 Thinking male 14.8556.1523.181.76.08 female 8.3457.84 Practical male 8.7059.7819.551.42.16 female -10.8458.81 Organized male -29.5753.56-9.72.76.45 female -19.8457.69 Patterns of Adaptive Learning Survey (composite score) Mastery male 18.185.64-2.50-2.18*.03 female 20.684.85 Approach male 11.885.70-.67.55.59 female 12.555.71 Avoidance male 10.434.15.32.36.72 female 10.114.03 Matrix Analogies Test Short Form (percentile ranking) male 65.2729.9213.832.41*.02 female 51.4425.85 Achievement (z-score) male .126.922.2681.33.19 female -.1421.09 Table 3-2. Means and standard deviations by grade Grade MSDM Differencet p-value What Is Happening In my Class (composite score) Task Orientation 9 32.269.112.401.51.13 10 29.869.54 Investigation 9 24.348.012.751.37.17 10 21.598.26 Equity 9 33.189.711.81.911.36 10 32.656.38 Student cohesiveness 9 33.085.39.53.803.43 10 32.505.34 Involvement 9 25.237.45.41.652.52 10 24.826.88 Teacher Support 9 28.866.70.81.304.76 10 28.058.07 Cooperation 9 27.589.28-1.07.133.89 10 28.657.03 Student Styles Questionnaire (T score) Thinking-feeling 9 10.9756.7921.341.62.11

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55 Table 3-2. Continued. Gender MSDM Differencet p-value Student Styles Questionnaire (T score) 10 -10.3758.28 Practical-imaginative 9 3.7660.02-14.08-1.11.27 10 10.3259.24 Extroversionintroversion 9 30.8656.41-13.14-.822.41 10 44.0045.17 Organized-Flexible 9 -22.9055.61-5.94.733.47 10 -28.8457.09 Patterns of Adaptive Learning Survey (composite score) Mastery 9 19.705.112.031.42.16 10 17.676.41 Approach 9 11.935.93-1.18-.792.43 10 13.114.66 Avoidance 9 10.323.98.16.164.88 10 10.164.50 Matrix Analogies Test Short Form (percentile ranking) 9 60.5630.444.37.721.48 10 56.1923.12 Achievement (percentage) 9 .004.99.011.482.28 10 -.0071.06 Race/Ethnicity and Family Income Effects A one-way ANOVA was conducted to compar e means between groups who identified themselves as Caucasian, African American, Hispanic, Asian/Pacific Islander, and South American, in ratings of classroom climate, motiv ation, learning styles pr eferences, aptitude, and achievement (Table 3-3). No significant differences were found on these variables for race/ethnicity. A one-way ANOVA was conducted to compare means between groups in ratings of classroom climate, motivation, learning styles preferences, aptitude and achievement who identified themselves as having a household in come from below twenty thousand annually to above sixty five thousand annually (Table 3-3). No significant differences were found on these variables for annual household income.

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56 Table 3-3. Tests for effects between groups by race/ethnicity and income Measure Race/Ethnicity Annual Household Income F p-value F p-value Cooperation 1.772 .11 .399 .75 Student Cohesiveness 1.704 .13 .331 .80 Task Orientation .414 .87 1.111 .35 Avoidance 1.072 .39 2.352 .08 Approach .262 .96 .204 .89 Mastery .538 .78 .221 .88 Extroversion 1.575 .178 1.165 .33 Practical .901 .50 .621 .60 Thinking .810 .57 .995 .40 Organized .319 .93 2.394 .08 Achievement 1.153 .34 .584 .638 Aptitude .805 .57 2.144 .10 A one-way ANOVA was conducted to compare means between groups in ratings of classroom climate, motivation, learning styles pr eferences, aptitude, and achievement for those students who reported mothers and fathers e ducation level from less th an higher school to advanced degrees. (Table 3-4). No significan t differences were found on these variables for parents education level. Table 3-4. Tests for effects betw een groups by parent education Measure Mothers Education Fathers Education F p-value Fp-value Cooperation .488.69.483.69 Student Cohesiveness 1.704.130.380.77 Task Orientation .373.77.848.47 Avoidance .429.731.904.14 Approach 1.530.21 .094.96 Mastery .134.94.579.63 Extroversion .931.44.119.95 Practical .853.47.495.69 Thinking 1.720.17.405.75 Organized .80.50.959.42 Achievement 1.437.23.584.63 Involvement 1.575.171.678.43 Aptitude 2.377.13.511.68

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57 Teacher and Class Period Effects A one-way ANOVA was conducted to compar e means between groups based upon class period and teacher in ratings of classroom cl imate, motivation, learning styles preferences, aptitude, and achievement (Table 3-5). No signi ficant differences were found on these variables. Table 3-5. Tests for effects between groups by teacher and class period Measure TeacherClass Period F p-value Fp-value Cooperation 1.730.191.628.17 Student Cohesiveness 3.056.08.380.24 Task Orientation .007.93.333.86 Avoidance 7.767.08.240.63 Approach 1.730.19 2.253.08 Mastery .2861.092.12.09 Extroversion .240.63.528.72 Practical .063.80.486.75 Thinking 1.471.23.437.78 Organized .572.45.601.66 Achievement 1.678.43.848.47 Involvement .604.22.240.63 Aptitude 1.468.22.134.94 Study One Students Ratings of Classroom Clim ate Will Predict Math Achievement The purpose of study one was to examine the eff ects of classroom climate on achievement. Using Pearsons correlation, th e three subscales for classroo m climate were Involvement, Cooperation, and Task Orientation (see Appendix B-1). Using multiple regression analysis, the contributions of student ratings of involvement, cooperation, and task orientation on math achievement were determined after controlli ng for aptitude, gender, grade level, and race/ethnicity (Table 3-6). This model accounted for 7.9% of the shared variance: F (103, 7) < 1.05, p < .40. Four non-significant contro l variables, gender, grade level, race/et hnicity, and aptitude were dropped from the subsequent analysis. Th e final classroom climate model (Table 3-7)

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58 accounted for 4.6% of the shared variance in math achievement: F (103, 7) < 1.40, p < .24. Involvement contributed signifi cantly to the prediction of achievement: t < -2.01, p < .04. Table 3-6. Classroom climate full mo del for prediction of achievement Subscale Betatp-value Involvement -.88-1.93.05Cooperation .731.43.15Grade .01.03.97Task Orientation .781.32.18Gender -.37-1.17.24Aptitude -.27-1.14.25Race/ethnicity -.11-.80.42Model Summary R R SquareFp-value .281 0791.05.40 Table 3-7. Classroom climate final mode l for the prediction of achievement Subscale Betatp-value Involvement -.89 -2.01 .04 Cooperation .62 1.24 .21 Task Orientation .23 .48 .62 Model Summary R R SquareFp-value .281 079 1.40 .24 Students Ratings of Motivation Will Predict Math Achievement Using multiple regression analysis, the cont ributions of student ratings of mastery performance, performance approach, and perf ormance avoidance on math achievement were determined after controlling for aptitude, gender, grade level, a nd race/ethnicity (Table 3-8). This model accounted for 4.3% of the shared variance in math achievement: F (103, 6) < .730, p < .63. Two non-significant control variables, gend er and grade level, were dropped from the subsequent analysis. The final motivation mode l accounted for 1.2% of the shared variance in math achievement: F (103, 6) < .307, p < .87 (Table 3-9). Table 3-8. Motivation full model fo r the prediction of achievement Beta t p-value Mastery .6151.425.16 Gender -.336-1.114.26 Grade -.265-.956.34 Approach -.228-.812.42 Avoidance .173.653.51

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59 Table 3-8. Continued Beta t p-value Aptitude .025.108.91 Model Summary R Rsquare Fp-value .208 .043 .730.63 Table 3-9. Motivation final model fo r the prediction of achievement Beta tp-value Approach -.290-1.048.29 Mastery .261.714.47 Aptitude .025.110.91 Avoidance .017.067.94 Model Summary R R Square Fp-value .111 .012.307.87 Study Two Students Preferred Learning Styles Will Predict Math Achievement The purpose of study two was to examine the ef fects of learning style preferences on math achievement. Using multiple regression analys is, the contributions of students preferred learning styles preferences on math achievement we re determined after co ntrolling for aptitude, gender, grade level, a nd race/ethnicity (Table 3-10). This model accounted for 10.6% of the shared variance in math achievement: F (103, 8) < .965, p < .47. Four non-significant control variables, gender, grade level, race/ethnicity, and aptitude were droppe d from the subsequent analysis. The final motivation model accounted for 6.1% of the shared variance in math achievement: F (103, 8) < 1.507, p < .21 (Table 3.11). Thinking -feeling learning style preference contributed significantly to the predicti on of math achievement: t < 2.248, p < .02. Table 3-10. Preferred learning styles full model for the prediction of achievement Beta t p-value Thinking-Feeling .2462.083.04 Extroversion-Introversion .1981.311.20 Aptitude .218.930.36 Race/Ethnicity -.033-.218.83 Grade -.204-.620.66 Gender -.133-.444.90 Practical-Imaginative .053.409.68 Organized-Flexible -.111-.811.42 Model Summary R R Square Fp-value .326 .106 .965.47

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60 Table 3-11. Preferred learning styles final model for the prediction of achievement Beta t p-value Thinking-Feeling .2602.248.02Extroversion-Introversion .087.740.46Organized-Flexible -.053-.454.65Practical-Imaginative -.001-.005.99Model Summary R R Square Fp-value .283 .0801.507.21 Contribution of a Confluence of Variables to Math Achievement beyond that Obtained Individually Using stepwise multiple regression analysis, th e unique contribution of classroom climate, motivation, and preferred learning styles on math achievement was determin ed after controlling for aptitude, gender, grade level, and race/ethni city (Table 3-12). The full model accounted for 40.1% of the shared variance in math achievement: F (103, 16) < 1.535, p < .13 (Table 3-13). Involvement correlated negatively with math ach ievement: t < -2.79, p < .00. Thinking-feeling correlated positively with math achievemen t: t < 3.13, p < .00. Non-significant terms were dropped, one at a time from the subsequent analysis The final model (Table 3-14) accounted for 19.1 % of the shared variance in math achievement: F (103, 3) < 5.193, p < .00. Two variable correlated positively with math achievement, th inking-feeling (t <2.809, p < .01) and cooperation (t < 3.17, p < .00), and one correlated nega tively, involvement (t < -3.23, p < .00). Table 3-12. Unique contribution of variab les to the predication of achievement Variable Contribution Cooperation .041Thinking-Feeling ..035Involvement .015Task Orientation .015Gender .007Extroversion-Introversion .006Race/ethnicity .006Aptitude .002Approach .002Mastery .001Organized-Flexible .000Avoidance .000Grade .000Teacher Support -.405

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61 Table 3-12. Continued Variable Contribution Practical-Imaginative .000Gender -.161Student Cohesiveness .000Investigation .000 Table 3-13. Full model of confluence of vari ables for the prediction of achievement Beta p-value Cooperation 1.9751.93.06Involvement -3.517-2.79.00Task Orientation .2141.48.15Thinking-Feeling 2.9283.13.00Extroversion-Introversion .2941.45.15Student Cohesiveness -1.260-1.39.17Race/ethnicity -.209-1.137.26Teacher Support -.405-.562.58Organized-Flexible .081.466.64Mastery .374.453.65Avoidance .162.410.68Practical-Imaginative -.059-.382.70Gender -.161-.356.72Aptitude .082.273.787Grade -.088-.231.818Approach -.063-.167.868Investigation -.105-.164.871Model Summary R R Square Fp-value .633 .4011.535.13 Table 3-14. Final model of confluence of va riables for the prediction of achievement Beta tp-value Involvement -1.683-3.231.00Cooperation 1.6483.166.00Thinking .3142.809.01Model Summary R R Square Fp-value .437 .1915.193.00

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62 CHAPTER 4 DISCUSSION The purpose of this study was to examine the effects of classroom climate, motivation, and students preferred learning styles on achievement. Students were asked to complete an aptitude test, an achievement pre-and post-test, a dem ographic survey, and measures of classroom climate, preferred learning styl es, and motivation. Data was co llected from 103 out of 150 ninth and tenth grade students enrolled in an Algebra I class. Test for effects by teacher, class period, grade level, household income level, race/ethni city, and parent educa tion level indicated no significant differences. Test for gender effects indicated that compared to boys, girls demonstrated higher aptitude, rated their classr oom more favorably for student cohesiveness and teacher support, and were more likely to re port an extroverted preference and a mastery performance orientation towards math. Howeve r, no significant gender differences were found for these qualifiers when pr edicting math achievement Hypotheses Students Ratings of Classroom Clim ate Will Predict Math Achievement The first goal of this study was to examin e the effects of classroom climate on math achievement. Three of the seven domains of the WIHIC with the strongest correlations to math achievement, involvement, cooperation, and task or ientation, were selected as measures of classroom climate when predicting math achieveme nt. A significant negative relationship was found for involvement with math achievement wh ile a significant positive relationship was found for cooperation. Task orientation did not cont ribute to math achievement. Thus, students perceive themselves as less likely to share thei r ideas or participate with the class, yet who worked together with peers on assignment comp letion, demonstrated higher achievement than those students reporting more class involvement a nd less peer interaction.

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63 The negative relationship between math achieve ment and involvement may be interpreted in developmental terms. Wigfield and collea gues (1998) noted a trend of increased efficacy coinciding with declining inte rest and value for math achie vement. Thus, students may demonstrate lower ratings of classroom climat e while demonstrating higher scores on math achievement. Because developmental trends we re not addressed, this trend may confound the variables measured in this study. This finding also may reflect the instructiona l style of math or expectations based upon prior experiences. Instructional styles in math classes typically rely on lectures followed by independent practice. Students who prefer to work independently may demonstrate a better fit with and benefit from this instructional style (Wetze l, Potter, & OToole, 1982). Given this finding, students with a preferen ce for introversion would be expected to perform better than those students with a prefer ence for extroversion. Al so, given this finding, students having a higher task orie ntation would be expected to be more successful in math. However, in this study, such results were not found. Extroversionintroversion and task orientation did not impact math achievement. One explanation may be due to the differences in measures used to assess achievement, motivation and learning style preferences. The measure used to assess achievement may not have been sufficiently sensitive to short term gains or may not have reflected the classroom curricula (AFT, 2001). Thus, the predictability of achievement by the variables used in this study may be underestimated. Additionally, the classroom climate scale for involvement does not measure the same qualities as those measur ed by the extroversion-introversion scale for learning styles preference. Invol vement measures the extent to which students perceive their own and others contribution to class discussion. Introversion assesses the extent to which

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64 students prefer to work independently. Thus, th e negative relationship of involvement with math achievement may be unrelated to students prefer ences. The findings may indicate students with greater adaptability for work, independent of the class instruction, rather than those with an introversion preference, perfor m better than those students wh o require more involvement. The finding that cooperation impacts math ach ievement appears to be contrary to the negative correlation of involvement with achievement. However, this is due to the differences in qualities measured by the respec tive domains. Involvement m easures the extent to which students participate with the class discussion a nd lecture while Cooperation measures the extent to which students help each other or work together. Thus, if the classroom allows for opportunities for students to give assistance to each other, this may increase positive ratings for this domain. Based upon these findings, it is sugg ested that students who are less inclined to participate with the class, yet who prefer to work together with peers to complete math assignments, demonstrate higher achievement gains than students who prefer to work alone or who more frequently are involved in class discussions and lectures. The nonsignificant impact of task orientation on math achievement may be related to other factors extraneous to the task or classroom goa ls. For example, students striving for future enrollment in a university may be responding to other goals beyond that which were measured by task orientation. Additionally, developmental tr ends indicate decreased interest and increased efficacy for math as students progress through secondary school. Thus, students extraneous goals as well as developmental trends may have underestimated the impact of task orientation on math achievement.

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65 Students Ratings of Motivation will Predict Math Achievement The second goal of this study was to examine the effects of students motivation when predicting math achievement, specifically their level of mastery performance, performance approach, or performance avoidance. These three motivational variables had no si gnificant impact on math achievement. Findings from prior studies indicated higher levels of motivation are associated with achievement (Davis, 2004; Middleton & Sp anias, 1999; Uguroglu, & Walberg, 1979). The current findings may reflect the complexities of mo tivation related to othe r factors unrelated to achievement specific to math. For example, th e methods used in this study did not assess external motivating factors such as achievement related to future college enrollment. College bound students often are motivated to achieve in most subjects in order to sustain a high grade point average required for competitive enrollment. Thus, the students may not be motivated to perform well in math, yet may be motivated to earn high scores to improve or maintain grade point averages (Higgins, Strauman, & Klei n, 1986; Dweck, 1992; Harackiewicz & Sansone, 1991). Students who are not college bound may ha ve other external motivational factors. Pressure from parents and peers may serve as motivation to avoid academic failure, rather than as motivation specific to math achievement. Ther efore, while students may not be motivated for the specific class, they may be motivated fo r a reason unrelated to the specific classroom (Emmons, 1992; Little, Lecci, & Watkinson, 1992). Students Preferred Learning Styles will Predict Math Achievement The third goal of this study was to examine th e effects of students preferred learning style on math achievement. A significant positive re lationship was found between thinking-feeling learning style preference and math achievement. These findings suggest students reporting a

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66 stronger thinking learning styles preference demonstrated larger increases in achievement than those students reporting a stronger feeling learni ng styles preference. Unlike academic domains that require creativ e or cooperative input, algebra I requires students to recognize, employ, and execute correct formulas for equations. Tasks requiring creativity and cooperation are more consistent wi th styles endorsed by those with a feeling preference. Tasks that require objective execution of formulas are more consistent with styles endorsed by students with a thinking prefer ence (Cano-Garcia & Hughes, 2000). While temperament is considered an innate and stab le trait (Bates, 1989; Benson, 2005; Buss & Plomin, 1984; Goldsmith et.al., 1987; Joyce, 2000; Kris tal, 2005; McCrae et. al ., 2000), learning styles are complex interactions between the indi vidual with his or her environment. During adolescence, attempts to delineate prefer ences with expectati ons of the classroom environment in a way that measures the effect on achievement may be difficult. Learning styles emerge from a confluence of cognitive, affectiv e, and biological factors (Keefe, 1991; Thomas, Chess, & Birch, 1968). Additionally, individual modes of perception, processing, memory, and cognition create a cognitive style for the individual based upon pr ior experiences (Keefe, 1991). Thus, according to Keefe, although pr eferences are inborn, they are subject to the influences of development and experience. Confounding the role of experi ence, gender differences in l earning styles also are noted (Eiszler, 2000), with girls reporting more reli ance on teacher instruction than boys. Thus, preference differences in modality and learning styles may have compounded the results for learning styles preferences. For example, give n the negative relationshi p of involvement with achievement, introversion could be expected to have a significant impact on math achievement. Yet, extroversion-introversion was found to be nonsignificant. Confounding these results,

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67 gender differences in both learning styles pref erences and math achievement were noted. Compared to females, males reported stronger ex troversion preferences and demonstrated higher math achievement. Thus, while classroom instru ctional style appears to be more conducive to those students with introver sion preferences, the results may have been confounded by the gender differences in modality preference. Becau se of the complex interaction of experience and temperament, future studies are needed to better understand the eff ects of learning style preferences as they relate to classroom achievement, both directly and indirectly. Contribution of a Confluence of Variables to Math Achievement Beyond that Obtained Individually The fourth goal of this study tested the hypothesis that the confluence of classroom climate, motivation, and preferre d learning styles w ould contribute to math achievement beyond that obtained when these variables were used individually. As exp ected, the significant contributing qualities predicted more variance in math achievement, in confluence, than when each quality is considered alone. Students re porting a stronger preference for thinking learning styles preference, more negative perceptions of involvement, and more positive perceptions of cooperation demonstrated higher math achieveme nt than those student s reporting a stronger feeling learning styles preferen ce, more positive perceptions of involvement, and less positive perceptions of cooperation. Motivati on did not predict math achievement. Although Davis, Davis, and Smith (2004) found classroom climate and motivation predict math achievement, their studies employed diffe rent measures with different theoretical underpinnings. For example, measures of rule clarity and order/organi zation of the Classroom Environment Scale (Moos & Tric kett, 1973) were used to measure classroom climate when predicting math achievement in middle school (Davis, 2004; Davis, Davis, & Smith, 2004). ). These qualities are not measured by the What Is Happening In this Class scale (Fraser, Fisher, &

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68 McRobbie, 1996). Eccles expectancy value model of motivation also predicted math achievement in middle school students (Davis 2004; Davis, Davis, & Smith, 2004). The qualities included in this model are not measured by the Patterns of Adaptive Learning Survey (Midgley, et al., 1996). Thus, these results sugg est a measures theoretic al considerations are important when assessing classroom climate and motivation as well as predicting math achievement. The results of this study rais e several important questions. What qualities of classroom climate are important when pred icting math achievement? In this study, no relationship was found for many factors of classr oom climate with achievement. However, in prior studies, aspects of classroom climate are significant in predicting achievement. Thus, an examination of individually identified quali ties of classroom climate impact the learner, whether it be academically, behaviorally, and/or socioemotiona lly may be beneficial. A better understanding of classroom climate would enable teachers to construct their classrooms in a manner that promotes classroom and instructional goals. Another important question is how does mo tivation affect achievement? Given the nonsignificant role of motivati on in predicting math achievement in this study, these findings do not suggest the lack of importance of motivation on achievement but rather the measures did not capture relevant motivational qualities important to students in algebra I classes. A comparative study of motivational measures on math achievement may shed some light on the adolescents motivation for math. An understanding of the complex interactions of the learner and th e learning environment specific to math may have important implications Trends that demonstrate reduced motivation

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69 for math achievement among adolescents and infe rior performance by females may be better understood through the developmental perspective of temperament. Implications An understanding of the contributions of indi vidual and environmen tal characteristics on the promotion of academic achievement is necessary to optimize the educational process. Academic achievement affects performance on mandatory statewide test scores, college placement exams, and grade point averages, and can have a functional impact on ones career and other life events. Ones level of achievement in high school may be a critical factor in acceptance into quality post-secondary educationa l settings and in competitive employment. However, other aspects of the classroom environment are important beyond academic achievement, such as the development of positiv e self-concept and socio-emotional well-being. Orientation toward competence (Harackiewi cz & Elliott, 1993), self-efficacy (Bandura, 1991), recognition of multiple goals (Barron & Harackiewicz, 2001), and the pursuit of various end states are important for understanding motiva tion for academic achievement (Boekaerts, de koning, and Vedder, 2006). Thus, students may addr ess conflicting goals that balance the need for socioemotional development with academic de velopment. Positive classroom climate has been associated with increas ed involvement(Alspaugh, 1998), f eelings of belonging (Goodenow, 1995; Osterman, 2000), social sati sfaction (Townsend & Hicks, 1997), improved quality of life within the classroom (DeYoung, 1977; Gottfreds on and Gottfredson, 1989; Hayes, Ryan, & Zeller, 1994;Mayer & Mitchell, 1993), increased self-efficacy (Bandura, 1991; House, 2002; Jackson, 2002), and positive self-concept (Crohn, 1983). Classroom climate has also been associated with higher attendance rates, lowe r drop out rates (Battistich & Horn, 1997; Resnick et al., 1997), and higher academic performance (Davis, 2004; Davis, Davis, & Smith, 2004; Goh & Fraser, 1995).

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70 Academic performance in high school math is reliant upon the development of complex processes that move toward high-level abstractio n and cognitive restructuring. For example, in algebra, students learn new m eanings to mathematical symbol s, learn to solve equations by operation rather than by left to right sequencing, a nd learn that a solution is not necessarily fixed nor is it numeric (Thomas & Tall, 2001). Thus students must acquire the ability to process algebraic information while contemplating the myri ad of possibilities or hypothetical values for the unknown x (Clement, 1982; Thomas & Tall, 2001). The acquisition and development of such ability is reliant upon the development of abstract and h ypothetical thinking (Piaget, 1985). In geometry, students must rely on visu al spatial processes to understand complex relationships among geometric shapes. Thus math readiness and math achievement may be more reliant on exposure to learning and opportunities for practice (Gamoran, 1987) than aptitude. For example, early exposure to mathematics may affe ct opportunities for learning as early as middle school. Exposure to math curriculum impacts le vel of achievement. Opportunities for learning differs through the stratification and quality of school coursework (Gamoran), as well as gender and race/ethnicity (Cambis, 1994), and socioeconomic status (Gamoran, 1987; Lee & Smith, 1997). Students who are low achieving are genera lly tracked into math classes that provide limited opportunity for learning new math skills Additionally, low achievi ng classes tend to be over representative of students in with low socio-economic status. Other factors related to opport unities for the development of higher order math skills for higher math achievement were school social comp osition related to racial/ethnic diversity (Lee & Bryk, 1989; Lee & Smith, 1997), variability in SES (Gamoran, 1987; Lee & Bryk, 1989; Lee & Smith), as well as school's academic emphasis, school climate (Chen & Stevenson, 1995), and school size (Lee & Smith, 1997). Smaller schools with less racial/e thnic diversity and

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71 overrepresentation of low SES (Lee & Bryk, 1 989; Lee & Smith) were associated with less variability in mathematic courses and lo wer math achievement (Lee & Bryk, 1989; Lee & Smith, 1997). Individual factors advocating higher math achievement were related to positive attitudes towards math achievement having the belief that effort, not aptitude is important, and high expectations from parents and peers (Chen & Stevenson, 1995). Thus, academic aptitude and prior math attainment may not be consistent predictors of high school math achievement (Clement, 1982; Thomas & Tall, 2001). As a result, many students may experience a decreased level of motivation resulting from the difficulty they encounter, leading to reduced feelings of academic self-efficacy (Pajares, & Graham, 1999; Wigfield, 1994). Low academic self-efficacy may result in fear of failure, which may lead students to avoidance achievement goals (Elliott, 1997; Pajares, & Graham, 1999). According to Elliott, the adoption of performance avoidance goals can ha ve long-term negative effects on end of the semester self-reports of wellbeing. Indeed, Boekaerts, de koning, and Vedder (2006) posit the avoidance goal oriented individu al must reference all possible fa ilure combinations in order to address avoidance at each level. Performance avoidance goals may contribute to performance anxiety and detract from completing immediate tasks successfully (Epste in, & Harackiewicz, 1992; Green, 1980; Trope, 1975). By contrast, students oriented toward pe rformance approach tend to seek out tasks that are challenging, provide the opportunity to demo nstrate competence, assess their ability, and provide feedback (Epstein, & Harack iewicz, 1992; Kuhl, 1978; Trope, 1975). Approach and avoidance goal orientations may be inherent traits, such as temperament (Elliott & Thrash, 2002). According to Elliott and Thrash, performance approach orientation

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72 was predicted by temperament characteristics, such as extroversion and positive emotionality, while negative emotionality and neuroticism were linked to perf ormance avoidance orientation. The importance of learning styles preference in achievement is evident. Previous studies have found learning styles affect achievement (Bajraktarevic, Hall, and Fullick, 2003; CanoGarcia & Hughes, 2000; Charkin, OToole, & Wetzel, 1985; Ennis-Cole, 2006). Although instructional styles are often adopted for thei r ease of execution in ma instream classes with varying abilities, the provision of greater congruence between the instructional style and learning style may enhance academic achievement and promote positive attitudes toward learning (Charkin, OToole, & Wetzel, 1985). Student achievement is higher when the instru ctional styles and materials are matched to the students preferred learning styles (Bajra ktarevic, Hall, & Fullick, 2003). Students studying economics who prefer to work independently, to follow established rules and procedures, and to execute pre-existing formula demonstrated high er achievement than students who prefer creative, collaborative, and flex ible styles (Cano-Garcia & Hughe s, 2000). In this study, students with a thinking preference displayed higher ma th achievement than those reporting a feeling preference. Qualities reported by thinking preferences appear to be more consistent with the style of classroom instructi on (Cano-Garcia & Hughes, 2000), the type of tasks required (Bajraktarevic, Hall, and Fullick, 2003), and the organizational structure of math classes (EnnisCole, 2006) in which competitive and individual e ffort is required while using preestablished formulas and facts. Understanding the role of temperament and prefer red learning styles in academic outcomes may generate classroom inte rventions designed to enhance the learning experience of all students.

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73 An improved understanding of student learning style preferences and teacher instructional style may uncover ways to better construct the cl assroom instructional environment to address the diversity of individual learning needs. Us ing temperament qualities to increase congruence between learning styles and t eaching styles may promote content mastery, enhance the acquisition of critical thi nking skills, and aid students in th e mastery of more complex content (Schroeder, 2006). Varied instru ctional practices provide a multimodal approach to teaching and help to address the varied needs of the individual students ( Ennis-Cole, 2006). Instructional practices are f ound to affect achievement. For example, teachers who focus on the development of student le arning with the goal of facilitating learning are more likely to plan effective lessons (Fernandez, Cannon, & Chokshi, 2003; Stewart & Brendefur, 2005). Lessons should be planned with an empathetic perspective to student learning (Lewis, Perry, & Murata, 2003; Stewart & Brendefur, 2005) by with identifying goals for students and gaining an understanding of how and why students lear n (Lewis, Perry, & Hurd, 2004; Stewart & Brendefur, 2005). Hawley & Valli (1999) suggest lessons plans should focus on closing the gap between student performances in relation to the expectations of educational outcomes. According to Stewart & Brendefur, the le sson plan model links teacher learning and knowledge to student learning through collaborati on teams. Teachers meet to share ideas, experiences, and knowledge (Lewis, Perry, & Hu rd, 2004; Stewart & Brendefur, 2005) for the creation of plans that address content and cont ext to maximize student learning and engagement while addressing student needs. Thus, the indivi dual characteristics of the teacher and students are incorporated into a dynamic approach to teaching and learning.

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74 CHAPTER 5 LIMITATIONS AND FUTURE STUDIES Various limitations may have attenuated the study of the relationship of classroom climate, motivation, and temperament on math achieveme nt. The nature of the sample population deserves attention. Although more diverse high schools were solicite d for participation, only one high school agreed to participate in this study. The participati ng school was ranked as an FCAT A school, indicating higher levels of academic accomplishment than lower B, C, or D rated schools. Additionally, the student population ma y be representative of higher socio-economic status than may have been found in a more di verse high school. Thus, the findings may not generalize to other school populations. The measures selected may constitute anothe r limitation. The measure of motivation may not have accounted for external motivational f actors, such as students value for math. Additionally, the FCAT sample test may not have been a sufficient measure for math achievement. The FCAT sample test is designed to be admi nistered at the beginni ng of the school year and re-administered prior to th e FCAT administration in early March. Thus, the measure should have reflected increases in know ledge and skills acquired in the classroom. However, some students demonstrated lower performance on the pos ttest compared to the pretest. This may indicate students guessed on one or both of the measures. This also may suggest the information measured on the sample test was not directly rela ted to classroom lessons and/or the test was not sufficiently sensitive to short term gains. Thus future studies may deci de to augment the range and sensitivity of instruments to identify and measure the contribu tion of these potentially salient factors when predicting math achievement.

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75 Lack of consideration for exte rnal influences may constitute another limitation. This study did not control for external qualities that may ha ve impacted student learning. For example, the importance of performing well on the FCAT, meeti ng parents expectations, or increasing or maintaining grade point average for college enro llment may have been mo tivational factors not considered. Including a measure of individual value for math ma y be an important factor for future studies. Students expectations for math class performance may have c ontributed to their ratings of classroom climate. For example, given the tr aditional instructional style of math that incorporates lecture with inde pendent practice, students may ra te the classroom involvement differently if asked to rate a political science class where expectations for classroom discussions may be higher. Inclusion of data on expectations for classroom climate compared to perceptions of actual classroom climate may provide a more co mprehensive measure to explain variability in achievement. Finally, this study did not addr ess developmental trends in achievement, motivation, and perceptions of classroom climate. For example, the age and grade levels selected for this study may have presented a confounding va riable not anticipated by th is study. Future studies may wish to assess the influence of these qualities on classroom climate perceptions, motivation, learning style preferences, and achievement. The results of this study raise many new questions for future studies. A better understanding of the role of multip le goals, the influence of prior experiences, the developmental status of the participan ts, the type of instruction, and the re adiness for math may be needed to better understand their impact on achievement. A dditionally, due to the developmental role of hypothetical thinking on algebra read iness, future studies may wish to examine the impact of

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76 classroom climate, motivation, and learning style preferences on bot h algebraic and nonalgebraic math achievement and achievement in other academic domains. Finally, although this study examined the relationship of preferred learning styles as they relate to math achievement. Future studies may see the value of examining th e effects of preferred le arning styles on ratings of classroom climate and motivation.

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77 APPENDIX A FORMS A-1 Consent to Participate Informed Consent to Participate Protocol Title: The effects of motivation, pr eferred learning styles, and perceptions of classroom climate on achievement in 10th grade algebra students Please read this consent document carefully before considering your ch ild as a participant in this study. If you wish your child to part icipate, please complete and return the consent form with your child Purpose of the research study: The purpose of this study is to examine the learning styles pr eferences, self-reported motivation for learning math, and percepti ons of classroom climate as it affects achievement for 10th grade algebra students. What your child will be asked to do in the study: If your child is allowed to participate, he/she wi ll be asked to complete a preand post-test in algebra, a nonverbal test of inte lligence, and surveys assessing th eir interest in math, level of motivation to succeed in math, and their opinion of the classroom environment for math. A short demographic survey will include rate of abse nces, socio-economic status, gender, age, and ethnicity. Your child will not have to respond to any questions they do not wish to and may withdraw from participation at any time, without penalty. The assessment will be administered during class time. Participation w ill not affect your childs grade. Time required: Time required for group administration is appr oximately 1.5 hours for each administration, a total of 3.0 hours. Risks and Benefits: While there are no risks for your child, benefits include a better understanding of the how and why students are inclined to perfor m better in areas of mathematics. Confidentiality: Your childs identity will be kept confidential to the extent provided by law. If you wish your child to participate, your childs informati on will be assigned a code number. The list connecting your childs name to th is number will be kept in a locked file. When the study is completed and the data have been analyzed, the list will be destroyed. Your childs name will not be used in any report. Voluntary participation: Your childs participation in this study is co mpletely voluntary. There is no penalty for not participating. Right to withdraw from the study: You have the right to withdraw your child fr om the study at anytime without consequence.

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78 Whom to contact if you have questions about the study: Susan Davis, Graduate Student, Department of Educational Psychology, University of Florida 392-0723 suedavis@ufl.edu Thomas Oakland, PhD, Department of Educa tional Psychology, Univers ity of Florida 392-0723 oakland@ufl.edu Whom to contact about your rights as a research participant in the study: UFIRB Office, Box 112250, University of Flor ida, Gainesville, FL 32611-2250; ph 392-0433. Consent to Participate I have read the procedure described above. I give permission for my child to participate ... Childs Name________________________________ Parent: _______________________________________Date: _________________

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79 A-2 Student Assent Form Student Assent Form To student: My name is Susan Davis and I am a graduate stud ent at the University of Floridas Department of Educational Psychology. I am conducting a study th at will be used to examine various factors that help students learn math. You have been asked to participate in a study that will be used to help iden tify these factors. If you agree to participate, you will be asked to co mplete a math test to determine achievement levels for algebra during class time. You will be asked to complete questionnaires that will help identify how you feel about learning math as well as how you perceive the math classroom. You are not required to answer any questions you do not wish to and you may withdraw from the study at any time without penalty. Y our participation or refusal to participate will not affect your grade. I have read the procedure described above. I agr ee to participate in th e procedure and I have received a copy of this description Name (please print) ___________________________________ Signature________________________________________ Date____________ Are you willing to participate in the research project? Yes______No______

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80 APPENDIX B MISCELLANEOUS TABLES Table 6-1. Descriptive statistics for full model of variables M SD Partial Corrrelations IQ (percentile) 59.65 29.02 .12 Achievement (zscore) 1.14 1.05 1.0 Temperament (T score) Extroversion 33.71 54.12 .10 Thinking 5.08 57.64 .26 Practical .47 59.77 -.02 Organized -25.47 55.17 -.07 Class Climate (Composites) Task Orientation 33.70 6.25 -.14 Equity 33.70 7.78 .02 Student Cohesiveness 32.90 5.49 -.05 Teacher Support 28.60 7.14 -.12 Cooperation 28.27 7.88 -.20 Involvement 25.14 7.37 -.21 Investigation 23.83 8.06 .04 Motivation (Composites) Mastery 19.28 5.43 .00 Approach 12.17 5.68 -.08 Avoidance 10.29 4.07 .02 Table 6-2. Means and standard deviations fo r final model of conf luence of variables Mean Composite ScoreSD Involvement 25.147.37 Cooperation 28.277.87 Mean T-score Thinking 5.07957.64

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81 LIST OF REFERENCES Acredolo, L. (1978). Development of spatial orientation in infants. Developmental Psychology, 13, 1. Aldridge, J. & Fraser, B. (1997). Examining science classroom e nvironments in a cross-national study. Proceedings Western Australian Inst itute for Educational Research Forum. Aldridge, J. & Fraser, B. (2000) A cross-cultural study of classroom learning environments in Australia and Taiwan. Learning Environment Research, 3, 101. Allen, D. & Fraser, B. (2002). Parent and st udent perceptions of th e classroom learning environment and its influence on student outco mes. Paper presented to the American Educational Research Associ ation. New Orleans, LA. Alspaugh, J. (1998). The relationship of school and community characteristics to high school dropout rates. The Clearinghouse, 71, 184. Altorf, M. (2005). Temperament and Metaphysic s: A Study on Jamess Pragmatism and Platos Sophistes. Paper presented at the Univer sity of Nijmegen, Nijmegen, Netherlands. American Federation of Teachers (2001). Making standards matter. American Educator, 25, 47. Ames, C. (1992). Achievement goals and classr oom motivational climate. In D. Schunk & J. Meece (Eds.) Students perception of the classroom (pp327-348). Hillsdale, NJ: Lawrence Erlbaum. Ames, C., & Archer, J. (1988). Achievement goa ls in the classroom: Students' learning strategies and motivation processes. Journal of Educational Psychology, 80 260. Anderman, E., Urdan, T., & Roeser, F. (2003). Th e Patterns of adaptive l earning survey, history, development, and psychometric properties. Pa per prepared for the Indicators of Positive Development Conference. Anderman, L. (1999). Classroom goal orient ation, school belonging and social goals as predictors of students' positive and negative affect following the transition to middle school. Journal of Research and Development in Education, 32, 89. Anderman, L. & Anderman, E. (1999). Social pr edictors of change in students achievement goal orientations. Contemporary Educational Psychology, 24, 21. Anderson, E. (2002). Individual levels of school belongingness predicted higher GPA, greater general optimism, negatively predicted depr ession, social regu lation, and behavior problems in school. In Motivating students, improving schools, advances motivation and achievement. Hillsdale, NJ: Erlbaum.

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82 Anderson, L., Stevens, D., Prawat, R., & Nickers on, J. (1988). Classroom task environments and students' task-related beliefs. Elementary School Journal, 88, 281. Ardilo, A., & Mareno, S., (2001). Directions in research in cross-cultural neuropsychology. Journal of Clinical and Expe rimental Neuropsychology, 77, 143. Aronson, E. (1999). The Power of self-per suasion. American Psychologist, 54, 875. Atkinson, J. (1980). Motivational determinants of risk taking behavior. In E. Higgins & A. Kruglanski (Eds.) Motivational science: social and personali ty perspectives. Ann Arbor, MI: Sheridan Brooks-Braun-Brumfield. Attaway, N. (2004). Understanding academic motiv ation in middle school students: Association with school belonging. Dissertat ion Abstracts Intern ational: Section B: the Sciences and Engineering, 64, 6314. Bajraktarevic, N., Hall, W., & Fullick, P. (2003). Incorporating learning styles in hypermedia environment: Empirical evaluation. Pape r presented at the Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems 2003. Nottingham, United Kingdom Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122. Bandura, A. (1986). Social foundations of t hought and action: A Social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Bardwell, R. (1984). The Development and motiva tional functions of exp ectations. American Educational Resear ch Journal 21, 461. Bargar, R. & Hoover, R. (1984). Psychological t ype and matching of cogni tive styles. Theory into Practice, 23, 56 Barron, K. & Harackiewicz, J. (2001). Achiev ement goals and optimal motivation: Testing multiple goal models. Journal of Personality and Social Psychology. 80, 706. Bassett, K. (2004). Temperament preferences fo r children ages 8 through 17 in a nationally represented sample. Dissertation presented at the University of Florida. Gainesville, FL. Bates, J. (1986). The measurement of temperament. In R. Plomin and J. Dunn (Eds.) The study of temperament: changes, continuities, and ch allenges (pp. 111). Hillsdale, NJ: Erlbaum. Bates, J. (1989). Applications of temperamen t concepts. In G.A. Kohnstamm, J.E. Bates & M.K. Rothbart (Eds.) Temperament in ch ildhood (pp 321). Chiche ster, England: John Wiley & Sons, Ltd. Bates, J. & Wachs, T. (1994). Temperament: in dividual differences at the interface of biology and behavior. Washington DC: Amer ican Psychological Association.

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83 Battistich, V., & Horn, A. (1997). The relationship between students' sense of their school as a community and their involvement in problem behaviors. American Journal of Public Health, 87, 1997. Beck, R. (1978). Motivation theo ries and principles. Englew ood Cliffs, New Jersey: PrenticeHall, Inc. Beilstein, C. & Wilson, J. (2000). Landmarks in route learning by girls and boys. Perceptual & motor skills, 91, 877. Benson, N. (2005). Cross-nationa l construct equivale nce of school-age childrens temperament types as measured by the student styles que stionnaire. Dissertation presented at the University of Florida. Gainesville, FL. Berens, L. (2000) Understanding yourself and others: An Introduction to temperament. London: Durtro Press. Black, A. (1965). On the Combination of dr ive and incentive motivation. Psychological Review, 72,310. Boekaerts, M., de koning, E., & Vedder, P. (2006) Goal directed behavior and contextual factors in the classroom: an innovative a pproach to the study of multiple goals. Educational Psychologist, 41, 33. Boeree, G., (2005). Carl Jung 1875. Persona lity Theories. E-text presented to Shippensburg University. Shippensburg, Pa. Bower, B. (2005). DNAs moody temperament. Science News 167, 308. Brophy, J. (2001). Motivating students to learn. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Brunstein, J. (1993). Personal goals and subjecti ve well-being: A Longitudinal study. Journal of Personality and Social Psychology, 65, 1061. Buss, D. & Plomin, R. (1984). Temperament: Earl y developing personality traits. Mahwah, NJ: Lawrence Erlbaum Associates. Cano-Garca, F., Hughes, E., (2000). Learning and Thinking Styles: an analysis of their interrelationship and influe nce on academic achievement. Educational Psychology, 20, 413. Calkins, S. & Fox, N. (1992). The relations among infant temperament, s ecurity of attachment and behavioral inhibition at twenty-f our months. Child Development, 63, 1456. Cambis, S. (1994). The path to math: gender and racial-ethnic differences in mathematics participation from middle school to hi gh school. Sociology of Education,67, 199.

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84 Casteel,C. (1998). Teacher-student interactions and race in integr ated classrooms. Journal of Educational Research, 92, 115. Chapin, S., & Eastman, K. (1996). External and internal characteristics of learning environments. Mathematics Teacher, 89, 112. Charkin, R., OToole, D., & Wetzel, J. (1985). Linking teacher and student learning styles with student achievement and attitudes. Th e Journal of Economic Education, 16, 111. Chatterji, M. (2004). Good and bad news about Florida student achie vement: Performance trends on multiple indicators since passage of th e A+ legislation. Educational Policy Brief. Doc No.EPSL-EPRU,Tempe,AZ: Edu cational Policy Studies Laboratory. Chatterji, M.(2005). Closing Floridas achievement gap. Jacksonville, FL: Florida Institute of Education at the University of North Florida. Chen, C. & Stevenson, H. (1995). Motivation a nd mathematics,: a comparative study of AsianAmerican, Caucasian American, and East Asia n high school students. Child Development, 66, 1215. Chess, S. & Thomas, A. (1996) Temperament th eory and practice. New York: Guilford Press Chess, S. & Thomas, A. (1996). Goodness of f it: Clinical applications from infancy through adult life. New York: Brunner/Mazel. Chionh, Y. & Fraser, B. (1998). Validation and use of the "What is Happening In This Class" Questionnaire in Singapore. Paper presen ted at Annual Meeting of the American Educational Research As sociation, San Diego, CA. Chipuer, H., Bramston, P. & Petty, G. (2003). De terminants of subjective quality of life among rural adolescents: a developmental perspectiv e. Social Indicators Research, 61, 79. Clement, J., (1982). Algebra word problem solutions: Thought proces ses underlying a common misconception. Journal for Research in Mathematics Education, 13, 16. Cornett, C. (1983). What you should know about teaching and learning styles. Bloomington, IN: Phi Delta Kappa Educational Foundation. Cranton, P. &, Knoop, R. (1995). Assessing Jung' s psychological types: The PET Type Check. Genetic, Social & General Psychology Monographs, 121, 249 Creemers, B. (1994). The effective classroom. London: Cassell. Crohn, L. (1983). Towards excellence: student and teacher behaviors predictors of school success. Research summary report presen ted at Northwest Regional Educational Laboratory. Portland, Or.

PAGE 85

85 Dart, B., Burnett, P., & Purdie, N. (2000). Students' conception of learning, the classroom environment, and approaches to learning. The Journal of Educational Research, 93, 262 270. Davis, H., Davis, S., & Smith-Bonahue, T. (2004). Exploring the social contexts of motivation and achievement: the role of relationship qual ity, classroom climate, and subject matter. Unpublished manuscript. Davis, S. (2004). The role of st udents perceptions of classroom climate in predicting academic motivation and assigned grades in middle schoo l mathematics. Thesis presented at the University of Florida. Gainesville, FL. Deci, E. & Ryan, R. (2002). Handbook of self -determination research. Rochester, NY: University of Rochester Press. Deci, E., Vallerand, R., Pelletier, L., & Ryan, R. (1991). Motivation and e ducation. Educational Psychologist, 26, 325. Denny, D., & Turner, R. (1969). Te acher characteristics, classr oom behavior, and growth in pupil creativity. The Elementary School Journal, 69, 265. Derryberry, D., & Reed, M. (1994). Temperament and attention: Orienting toward and away from positive and negative signals. Journal of Personality and Social Psychology, 66, 1128. Dweck, C. (1992). The study of goals in psychology. Psychological Science, 3, 165. Dweck, C. (1999). Self-theories: their role in motivation, pe rsonality, and development. Philadelphia, PA: Psychological Press. Dweck, C. & Leggett, E. (1988). A socio-cognitiv e approach to motivation and personality. Psychological Review, 95,256. Eccles, J. (1983). Expectancies, values, and academic behaviors. In J.T.Spence (Ed.), Achievement, and Achievement Motives: Psyc hological and Sociologi cal Approaches (pp. 75). San Francisco: W.H.Freeman. Eccles, S., Wigfield, A., Midgely, C., Reuman, D., MacIver, D., & Feldlaufer, H. (1993). Negative effects of traditional middle school s on students' motivation. The Elementary School Journal, 93, 555. Eccles, J., Wong, C., & Peck,S. (2 006). Ethnicity as a social construct for the development of African American adolescents. Journal of School Psychology,44, 407. Ellis, S. (1996). Staff development is the key to school reform: an interview with Efrain Vila. Journal of Staff Development, 17, 52.

PAGE 86

86 Elliott, A. (1997). Avoidance achievement motivation: a personal goals analysis. Journal of Personality and Social Psychology, 73, 171. Elliot, A., & Church, M. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personal ity and Social Psychology, 72, 218. Elliot, A., & Harackiewicz, J. (1996). Approach and avoidance achievement goals and intrinsic motivation: A Mediational anal ysis. Journal of Personal ity and Social Psychology, 70, 461. Elliot, A. & McGregor, S. (2001). A 2x2 achieve ment goal framework. Journal of Personality, 80, 501. Emmons, R. (1986). Personal strivings: An appr oach to personality a nd subjective well-being. Journal of Personality a nd Social Psychology, 51, 1058. Emmons, R. (1992). Abstract vers us concrete goals: Pe rsonal striving level, physical illness, and psychological well-being. Journal of Personality and Social Psychology, 62, 292 Ennis-Cole, D. (2006). Positive learning places. Presented at the Texas Computer Education Association Conference, Richardson, TX. Epstein, J. A., & Harackiewicz,J. M.(1992). Wi nning is not enough: The Effects of competition and achievement orientation on intrinsic in terest. Personality and Social Psychology Bulletin, 18, 128.http://psycinfo.apa.org.lp.hscl.ufl.edu/doi/getuid.cfm?uid=197609902-001 Epstein, S. (1994). Integration of the cogni tive and psychodynamic unconscious. American Psychologist, 49, 707. Fernandez, C., Cannon, J. & Chokshi, S. (2003). A US-Japan lesson study collaboration reveals critical lenses for examining practice. Teaching and Teacher Education 19, 171 Ferrar, M. (1994). Validity of the STAR: student styles questi onnaire: racial-ethnic comparisons. Presented at the Univers ity of Texas at Austin. Austin, TX. Ford, M. (1992). Motivating hum ans: goals, emotions, and personal agency beliefs. Newberry Park, CA. Fraser, B. (1986). Classroom environm ent. Dover, NH.: Croom Helm Ltd. Fraser, B. (1988). Classroom environment instrume nts: development, validity, and applications. Learning Environments Research An Internationa l Journal, 7, 1. Fraser, B. (2002). Learning environment research : Yesterday, today and tomorrow. In Goh,S. & Khine, M. (Eds.), Studies in educational learning environments: an international perspective, 1. Singapore: World Scientific.

PAGE 87

87 Fraser, B., Fisher, D. & McRobbie, C. (1996). Development, validation and use of personal and class forms of a new classroom environment instrument. Paper presented at the annual meeting of the American Educatio nal Research Association: NY. Fraser, B. & Walberg, H. (1991). Educationa l environments: evaluation, antecedents, and consequences. Elmsford, NY: Pergammon Press, Inc. Freeman, K. (2004). The signifi cance of motivational cultur e in schools serving African American adolescence: A goal theory approac h. In Pintrich, P & Maehr, M. (Eds.) Motivating students, improving schools, advan ces motivation and achievement, the legacy of Carol Midgely, 13,(pp 65). Toronto, Ontario: Elsevier, Ltd. Gaith, G. (2003). The relationship between form s of instruction, achievement, and classroom climate. Educational research, 45, 83. Gallagher, A. & Kaufman,J. (2005). Gender differences in mathematics. New York: Cambridge University Press. Gamoran, A. (1987). The stratif ication of high school learni ng opportunities. Sociology of Education, 3, 135. Gamoran, A., Porter, A., Smithson, J., & White, P. (1997). Upgrading high school mathematics instruction: improving lear ning opportunities for low achieving, low income youth. Educational Evaluation and Policy Analysis, 19, 325. Gardner, A., Mason, C., & Matyas, M. (1989) Equity, excellence and "just plain good teaching. The American Biology Teacher, 51, 72. Geary,D., Saults,S., Liu,F. & Hoard,M. ( 2000). Sex differences in spatial cognition, computational fluency, and arithmetic reas oning. Journal of Experimental Child Psychology, 77, 337. Goh, S., & Fraser, B. (1995). Learning e nvironment and student outcomes in primary mathematics. Paper presented at the A nnual Meeting of the American Educational Research Association. San Francisco, CA. Gottfredson, G., & Gottfredson, D. (1989). School climate, academic performance, attendance, and dropout. US Dept of Education. Goldsmith, H., Buss, A., Plomin, R., Rothbart, M ., Thomas, A., Chess, S., Hinde, R., McCall, R. (1987). Roundtable: What is temperament? Four approaches. Child Development, 58, 505. Goodenow, C. (1993a). The ps ychological sense of school me mbership among adolescents: scale development and educational corre lates. Psychology in the schools, 30, 79. Goodenow, C. (1993b). Classroom belonging among ear ly adolescent students. Journal of Early Adolescence, 13, 21.

PAGE 88

88 Goodenow, C. (1995). Conceptualizing and m easuring classroom belonging and support among adolescents. Unpublished paper presen ted to Tufts University. Boston, MA. Gouteux, S. & Spelke, E. (2001). Childrens us e of geometry and landmarks to reorient open space. Cognition, 81, 119. Griffin, S. & Case,R. (1996). Evaluating the breadt h and depth of training effects when central conceptual structures are ta ught. Monographs of the Society for Research in Child Development, 61, 83. Haertel, G., Walberg, H., & Haertel, E. (1981). Social-psychological environments and learning: a quantitative synthesis. British Ed ucational Research Journal, 7, 27. Hagen, A. & Weinstein, C. (1995). Achievem ent goals, self-regulat ed learning, and the classroom context. New Directions for Teaching for Learning, 63, 43. Halpern, D. (2000). Sex differences in cognitiv e abilities (3rd ed.). Mahwah, NJ: Erlbaum. Harter, S., & Connell, J. P. (1984). A model of children's achieveme nt and related selfperceptions of competence, control, and motiv ational orientation. In J. Nicholls & M. Maehr (Eds.), Advances in motivation and achievement, 3. The development of achievement motivation. London: JAI Press. Harackiewicz, J., & Manderlink, G. (1984). A pro cess analysis of the effects of performancecontingent rewards on intrinsic motivation. Journal of Experimental Social Psychology, 20, 531. Harackiewicz, J., & Sansone, C. (1991). Goals and intrinsic motivation: You can get there from here. Advances in Motivation and Achievement, 7, 21. Hawley, D. & Valli, L. (1999). The essentials of effective professional development: a new consensus. In L. Darling-Hammond & G. Sykes (Eds.) Teaching as the learning profession. Handbook of policy and practice (pp 127). San Fransicso: JosseyBass Publishers. Hayes, S. (1999). Comparison of the Kaufman Br ief Intelligence Test and the Matrix Analogies TestShort Form in an Adolescent Forensic Population. Psychological Assessment, 11, 108. Heider, F. (1958). The psychology of inte rpersonal relations. Wiley: New York. Herner, L. & Spelke, E. (1994). A geometric pr ocess for spatial reorie ntation in young children. Nature, 370, 57. Hespos, S. & Rocaht, P. (1997). Dynamic representation in infancy. Cognition, 64, 153.

PAGE 89

89 Higgins, E. T., Strauman, T., & Klein, R. (1986). Standards and the proce ss of self-evaluation: Multiple affects from multiple stages. Handbook of motivation and cognition: Foundations of social behavior, 1, 23. Horton, C. & Oakland, T. (1997). Temperamentbased learning styles as moderators of academic achievement. Adolescence, 32, 131. House, D. (2002). The independent effects of st udent characteristics and instructional activities on achievement: an application of the input -environmentoutcome assessment model. International Journal of In structional Media, 29, 225 Hunt, J. (1965). Intrinsic motivation and its role in psychological development. In D. Levine (Ed.), Nebraska symposium on motivation. Linc oln, NE : University of Nebraska Press. Hunus, R., & Fraser, B.J. (1997). Chemistry learning Environment in Brunei Darussalam's secondary Schools. In D.L. Fisher., & T. Ri chards. (Eds.), Science, Mathematics and Technology Education and National Development: Proceedings of the Vietnam conference (pp.108). Hanoi; Vietnam. Hyde, J. (2005). The gender similarities hypo thesis. American Psychologist, 60, 581. Jackson, D., Ahmed,S., & Heapy, N. (1976). Is ac hievement a unitary construct? Journal of Research in Personality,10,1. Jackson, J. (2002). Enhancing self-efficacy a nd learning performance (motivation and social processes). The Journal of Experimental Education, 70, 243. Jacobson, L. (2000). Valuing dive rsity-student-teacher relationshi ps that enhance achievement. Community College Review, 28, 49. Johnson, L., Lutzow, J., Strothoff, M., & Zanni s, C. (1995). Reduci ng negative behavior by establishing helping relationshi ps and a community identity program. Rockford Ill. Joyce, D. (2000). Temperament based learning styles of children with conduct disorder and oppositional defiant disorder. Dissertation pres ented at the University of Florida. Gainesville, Fl. Joyce, D. & Oakland, T. (2005). Temperam ent differences among children with conduct disorder and oppositional defiant disorder. The California School Psychologist, 10, 125 136. Juarez, A. (2000). Enhancing student performance through cla ssroom motivation. Dissertation Abstracts (ERIC Document Repr oduction Service No. ED458298). Jung, C. (1971). Psychological types (H. G. Baynes, Trans.). Prin ceton, NJ: Princeton University Press. (Original work published 1921).

PAGE 90

90 Kalin, N., Larson, C., Shelton, S., Davidson, R. (1998). Assymetrical frontal brain activity, cortisol, and behavior associat ed with fearful temperament in rhesus monkeys. Behavioral Neuroscience, 112, 286. Kakman, D. (2004). Motivational role of prim ary students perceptions of their classroom experience. Dissertation presented at Northern Illinois University. Kaplan, (2000). Achievement goals and intergroup relations. In P. Pintrich & D. Schunk (Eds.) (2nd Ed.) (2002) Motivation in education; theory, researc h, and application (p. 97). Upper Saddle River, NJ: Merrill Prentice-Hall. Karnes, F. A., & McGinnis, J. (1994). Correla tions among the scores on the Matrix Analogies Test Short Form and the WISC-R with gi fted youth. Psychological Reports, 74, 948. Kaufman, A., & Kaufman, N. (1990). Kaufman Brief Intelligence Te st Manual. Circle Pines, MN: American Guidance Service. Kaufman, A., McClean,J., & Reynolds, C. (1998). An alysis of WAIS-R f actor patterns by sex and race. Journal of Clinical Psychology, 47, 548. Keirsey, O. & Bates, M. (1984). Please understa nd me: character and temperament types. (5th ed.). Del Mar, CA: Promet heus Nemesis Book Company. Keyser, V., & Barling, J. (1981). Determinants of children's self-efficacy beliefs in an academic environment. Cognitive Therapy Research, 5, 29. Khine, M., & Fisher, D., (2001). Classroom e nvironments and teachers cultural background in secondary science class in an Asian contex t. Paper presented at the International Educational Research Conference, Univer sity of Notre Dame, Fremantle, Western Australia. Khoo, H. & Fraser, B. (1997). Using classroom environment dimensions in the evaluation of adult computer courses in Singapore. Paper presented at Annual Mee ting of the American Educational Research Associ ation, Chicago. Chicago, IL. Kim, H., Fisher, D., & Fraser, B. (2000). Classroom environment and teacher interpersonal behavior in secondary science classes in Korea. Evaluation and Research in Education, 14, 3 Kolb, D. (1984). Experiential learning: Experi ence as a source of learning and development. Englewood Cliffs, NJ: Prentice-Hall. Kristal, J. (2005). The Temperament Perspective. Baltimore, MD: Paul H. Brooks Publishing Co. Kunc, N. (1992). The need to belong: rediscovering Maslow's hierarchy of needs. In R. Villa, J. Thousand, W. Stainback, & S.Stainback (Eds.) Restructuring for caring and effective education. Baltimore, MD: Paul H. Brookes

PAGE 91

91 Lawrence, G. (1982). People type s and tiger stripes: a practical guide for learning styles. (Second edition) Center for application of psychological types: Gainesville, FL. Learmouth,A., Nadel,L., & Newcombe, N. (1999). Childrens use of landmarks: Implications for modularity theory. Ps ychological Science, 13, 337. Lee, J. (2002). Racial and ethnic achievement gap trends: re versing progress toward equity? Educational Researcher, 31, 3. Lee, E. & Bryk, A. (1988). Curriculum tracking as mediating the social distribution of high school achievement. Sociology of Education, 61, 78 Lee, E. & Bryk, A. (1989). A multilevel mode l of the social distribution of high school achievement. Sociology of Education, 62, 172. Lee, V. & Smith, J. (1997). High school size: which works best and for whom? Educational Evaluation and Policy Analysis, 19, 205. Levy, N., Murphy, C., & Carlson, R. (1972). Pers onality types among Negro college students. Educational and Psychological Measurement, 32, 641. Lewis, C., Perry, R. & Hurd, J. (2004). A deep er look at lesson study. Educational Leadership, 2, 18 Lewis, C., Perry, R., & Murata, A. (2003). Lesson study and teacher knowledge development: collaborative critique of a research mode l and methods. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago, IL. Little, B., Lecci, L., & Watkinson, B. (1992). Personality and personal projects: Linking Big Five and PAC units of analysis. Journal of Personality, 60, 501. Lipman, L. & Moore, K. (2005). Why do childre n need to flourish? Conceptualizing and measuring indicators of positive development. New York: Springer Publishing. Logon, H. (1968) Incentive theory, and change s in reward. In G.H. Bower (Ed.) The psychology of learning and motiv ation. New York: Academic. Madden, L. (1997). Motivating students to lear n better through own goa l setting. Education, 117, 411. Maehr, M. & Nicholls, J. (1980). Culture and achievement motivation: A second look. In N. Warren (Ed.), Studies in cro ss-cultural psychology (pp. 221-267). New York: Academic McClelland, D. (1985). How motives, skills, an d values determine what people do. American Psychologist, 40, 812. McClelland, D. (1987). Human motivation. Ca mbridge, MA: Cambridge University Press.

PAGE 92

92 McClelland, D. & Koestner, R., & Weinberger, J. (1989). How do self-attributed and implicit motives differ? Psychology Review, 96, 690. McCrae, R., Costa, P., Ostendorf, F., Angleitner, A., H eb kov, M., Avia, M., Sanz, J., Snchez-Bernardos, M., Kusdil, M., Woodfie ld, R., Saunders, P., & Smith, P. (2000). Nature Over Nurture :Temperament, Personalit y, and Life Span Development. Journal of Personality and Social Psychology, 78, 173. McRobbie, C., & Fraser, B. (1993). Associati ons between student outcomes and psychosocial science environment. Journal of Educational Research, 87, 78. Meyers, I. & McCaulley, M. ( 1985). Manual: A guide to the de velopment and use of the MyersBriggs Type Indicator. Palo Alto, CA : Consulting Psychologists Press. Middleton, J. & Spanias, P. (1999). Motivati on for achievement in mathematics: findings, generalizations, and criticisms of the resear ch. Journal for research in Mathematics Education, 1, 65. Midgley, C. (2002). (Ed.) Goal Structures a nd patterns of adaptive le arning. Hillsdale, NJ: Lawrence Erlbaum. Midgley, C., Feldlaufer, H., & Eccles, J.(1989). Student-teacher relations and attitudes toward mathematics before and after transition to junior high school. Ch ild Development, 60, 981. Midgley, C., Maehr, M., Hicks, L., Roeser, R., Urdan, T., Anderman, E., & Kaplan, A. (1996) Patterns of Adaptive Learning Survey (PALS). The University of Michigan. Flint, MI. Moos, R. (1979). Evaluating educational outcomes San Francisco, CA: Jossey-Bass Publishers. Moos, R. & Trickett, E. (1974). Classroom environment scale manual. Palo Alto, CA: Consulting Psychologists Press. Mormede, P., Courvoisier, H., Ramos, A., Mari ssal-Arvy, N., Ousova, O., Desautes, C., Duclos, M., Chaouloff, F., Moisan, M. (2002). Mol ecular genetic approach es to investigate individual variations in behavioral and neuroendoc rine stress responses. Psychoneuroendocrinology, 27, 563. Myers, I. B. (1962). The Myers-Briggs Type Indicator [manual]. Prin ceton, NJ: Educational Testing Service. Myers, I., McCaulley, M., Quenk, l., & Hammer, A. (1998). MBTI manual: A guide to the development and use of the Myers-Briggs T ype Indicator (3rd ed.) Palo Alto, CA: Consulting Psychology Press. Myhill, D. & Jones, S. (2006). She doesnt sh out at no girls. Pupils perceptions of gender equity in the classroom. Camb ridge Journal of Education, 36, 99.

PAGE 93

93 Naglieri, J. (1985). Matrix an alogies test-short form. San Antonio, TX: The Psychological Corporation. Newman, L. (1985). Hemisphere socializa tion and Jungian typological evidence for a relationship. Bulletin of Psychological Type, 10, 2, 13. Nicholls, J. (1992). Students as educational theorists In D. Sc hunk & J. Meece (eds.) Students perception of the classroom (p267). Hillsdale, NJ: Lawrence Erlbaum. Oakland, T., Alghorani, A., & Lee, D. (2007) Temperament-Based Learning Styles of Palestinian and US Children. Sc hool Psychology International,28, 110 Oakland, T., Glutting, J., & Horton, C. (1996). Student styles questionnaire. San Antonio, TX: The Psychological Corporation. Oakland, T. & Lub, L. (2006). Temperament styl es of children from the People's Republic of China and the United States. Sc hool Psychology International, 27, 192. Oakland, T., Joyce, D., Glutting, J., & Horton, C. (2000). Temperament-based learning styles of male and female students identified as gifted and students not identified as gifted. Gifted Child Quarterly, 44, 183. Oakland, T., Stafford, M., Horton, C., & Glutti ng, J. (2001). Temperament and vocational preferences : age, gender, and racial-ethnic comparisons using the Student Styles Questionairre. Journal of Career Assessment 9, 297. Osterman, K. (2000). Students' need for bel onging in the school community. Review of Educational Research, 70, 323. Overall, J., & Levin,H., (1978). Correcting for cultural factors in evaluating cultural deficit on the WAIS. Journal of Clinical Psychology, 34, 910. Pajares, F. & Graham, L. (1999). Self effi cacy, motivation constructs, and mathematical performance of entering middle school stude nts. Contemporary Educational Psychology, 24, 124. Paolo, A., Ward, L., Ryan, J., & Hilmer, C., ( 1996). Different WAIS-R short forms and their relation to ethnicity. Personal and Individual Differences, 6, 851-856. Paris, S., Olson, G. & Stevenson, H. (1983) (Eds .) Learning and motivation in the classroom. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Pintrich, P. (2000). The role of goal orientation in self-regulated learning. In M. Boedaerts, P. Pintrich, & M. Zeidner (Eds.). Handbook of self-regulation: theory, research, and application (pp.451). San Diego, CA: Academic Press. Pintrich, P. & De Groot, E. ( 1990). Motivational a nd self-regulated lear ning components of classroom academic performance. Journal of Educational Psychology, 82, 33.

PAGE 94

94 Pintrich, P. & De Groot, E. (1994) Classroom and individual di fferences in early adolescents' motivation and self-regulate d learning, Journal of Early Adolescence, 14, 139. Prewett, P. (1995). A comparison of two screenin g tests (the Matrix Anal ogies Test Short Form and the Kaufman Brief Intelligence Test) wi th the WISCIII. Psychological Assessment, 7, 69 Prewett, P. & Farhney, M. (1994). The concurrent validity of th e Matrix Analogies Test Short Form with the Stanford-Binet: Fourth Ed ition and KTEABF (Academic Achievement). Psychology in the Schools, 31, 20 Proctor, C. (1984). Teacher expectations: a m odel for school improvement. Elementary School Journal, 84, 469. Punnett, B. (1986). Goal setting and performan ce among elementary school students. Journal of Educational Research 80, 40. Resnick, M., Bearman, P., Blum, R., Bauman, K., Harris, K., Jones, J., Tabor, J., Beuhring, T Sieving, R., Shew, M., Ireland, M., Bearin ger, L. & Udry, J. (1997). Protecting adolescents from harm. JAMA, 278, 823-832. Rhodes, C. & Houghton-Hill, S. (2000). The lin kage of continuing professional development and the classroom experience of pupils, barrie rs perceived by senior managers in some secondary schools. Journal of In-Service Education, 26, 423. Richman, C., Boelsky, S., Koovand, N., Vacca, J., & West, T. (1997). Racism 102: The Classroom. Journal of Black Psychology, 23, 378. Riser, J. (1979). Spatial orientation in si x month old infants. Child Development, 50, 10781087. Robinson, R. & Carrington, S. (2002). Professi onal development for inclusive schooling. International Journal of Educational Management,16, 239. Rosenkrantz, P., Vogel, S., Bee, H., Broverm an, I. & Broverman, D. (1968). Sex-role stereotypes and self-concepts among college stud ents. Journal of Consulting and Clinical Psychology, 32, 287. Rothbart, M., Derryberry, D., Posner, M. ( 1994). A psychobiological approach to the development of temperament. In Bates & Wachs (Eds.) Temperament: individual differences at the interface of biology and behavior. Washington DC: American Psychological Association. Rotter, J. (1966). Generalized expectancies for inte rnal versus external cont rol of reinforcement. Psychological Monograph, 80, 1. Rowe, D. & Plomin, R. (1981). Temperament in early childhood. J ournal of Personality Assessment, 41, 150.

PAGE 95

95 Ryan, R. & Deci, A. (2000). Self-determination theory and facilitation of motivation, social development, and well-being. American Psychologist, 55, 68. Ryan, R., Deci, A. & Grolnick, W. (1995). Autono my, relatedness, and the self: their relation to development and psychopathology. In D. Cich etti & D.J. Cohen (Eds.). Developmental psychopathology Vol 1: Theory and Met hods. New York: John Wiley and Sons. Ryan, J., Glass, L., & Brown, C. (2007). Administration time estimates for Wechsler Intelligence Scale for Children IV subtest, co mposites, and short forms. Journal of Clinical Psychology, 63, 309. Saft, E. & Pianta, R. (2001). Teachers perceptions of their relationships w ith students: effects of child age, gender, and ethnici ty of teachers and children. School Psychology Quarterly, 16, 125. Schurr, K., Rubie, V., Palomba, C., Pickerill, B., & Moore, D. (1997). Relationships between MBTI and selected aspects of Tintos model fo r college attrition. Journal of Psychological Type, 40, 109. Shelton, J. (1996). Health, stress, and coping. In A.L. Hammer (Ed.), MBTI applications: A Decade of research on the My ers Briggs Type Indicator (pp.195). Palo Alto, CA: Consulting Psychologists Press. ShuttleworthEdwards, A., Kemp, R., Rust, A ., Hartman, N., & Radloff, S. (2004). Crosscultural effects on IQ test performance: A re view and preliminary normative indications on Wais-III test Performance. Journal of C linical and Experimental Neuropsychology, 26, 903. Slate, J., Graham, L., & Bower, J. (1996). Re lationships of the WISC-R and K-BIT for an adolescent clinical sample. Adolescence, 31, 777. Spelke, E. (2005). Sex differences in intrinsic aptitude for mathematics. American Psychologist, 60, 950. Stringfield, S. (1994). A model of elementary school effects In Advances in school effectiveness research and pr actice. Oxford: Pergamom. Schunk, D. (1981). Modeling and at tributional effects on children's achievement: A self-efficacy analysis. Journal of Educational Psychology, 73, 93 Suedfeld, P. & Epstein, Y. (1970). Where is the D in dissonance? Jo urnal of Personality, 71, 39,178 Stafford, M. (1994). Validity of the STAR: student styles questi onnaire: racial-ethnic comparisons. Dissertation presented at the Un iversity of Texas at Austin. Austin, TX.

PAGE 96

96 Sternberg, G. (1990). Brain and personality: ex troversion/introversion in relation to AEEG, evoked potentials and cerebral blood flow. U npublished doctoral dissertation, University of Lund, Sweden. Stewart, R. & Brendefur, J. (2005). Fusing lesson study and auth entic achievement: A model for teacher collaboration. Phi Delta Kappan, 86, 681. Tomarken, A., Davidson, R., Wheeler, R., & Kinne y, L. (1992). Psychometric properties of resting anterior EEG asymmetry: Temporal stability and internal consistency. Psychophysiology, 29, 576. Thayer, B. (1996). The Relationship of temp erament with respect to age, gender, and race/ethnicity in children and adolescents. Dissertation presented at the University of Texas at Austin. Austin, TX. Thomas, M., & Tall, D. (2001). The Long term cognitive development of symbolic algebra. International Congress of Mathematical Inst ruction (ICMI) Working Group Proceedings The Future of the Teaching and Learning of Algebra, Melbourne, 2, 590597. Thomas, A., Chess, S., & Birch, H. (1968). Temp erament and behavior disorders in children. New York: New York University Press. Townsend, M., & Hicks, L. (1997). Classroom go al structures, social; satisfaction and the perceived value of academic tasks. Britis h Journal of Educational Psychology, 67, 1. Trope, Y. (1975). Seeking information about one' s own ability as a determinant of choice among tasks. Journal of Personality and Social Psychology, 32, 1004. Uguroglu, M. & Walberg, H. (1979). Motivation a nd achievement: A quantitative synthesis. American Education Research Journal, 16, 375. Urdan, T. & Maehr, M. (1995). Beyond a two-go al theory of motivation and achievement: A case for social goals. Review of Educational Research, 65, 213. Voyer, D., Voyer, S., & Bryden, M., (1995). Magnit ude of sex differences in spatial abilities: A meta-analysis and considerati on of critical variables. Psychological Bulletin, 117, 250 270. Walberg, H. (1979). (Ed) Education environm ents and effects. Berkely, CA: McCutchan Publishing Corporation. Wang, M., Haertel, G., & Walberg, H. (1993). What helps students learn. Educational Leadership, 51, 74. Wechsler, D. (2003). Wechsler Intelligence S cale for Children-Fourth Edition: Administrative and scoring manual. San Antonio, TX : The Psychological Corporation.

PAGE 97

97 Wigfield, A. (1994). Expectancy value theory of achievement motivation: a developmental perspective. Educational Psychology Review, 6, 49. Wilms, J. (2003a). Student engagement at school : A sense of belonging and participation. Paris: OECD Wilms, W. (2003b). Altering the structure and culture of American public schools. Phi Delta Kappan, 84, 606 Wechsler, D. (1991). Wechsler Intelligence Scale for ChildrenThird Edition. San Antonio, TX: Psychological Corporation Weiner, B. (1985). An Attribu tional theory of motiv ation and emotion. Psychological Review, 92, 548. Weiner, B. & Kukla, A. (1970). An attributional analysis of achievement motivation. Journal of Personality and Social Psychology, 15, 1. Wetzel, J., Potter, W. & OToole, D. (1982). The Influence of learning and teaching styles on student attitudes and achievement in the in troductory economics course: a case study. Journal of Economic Education, 13, 33. Whiting, B. & Whiting, J. (1975). Children of sex cultures: a psychocultural analysis. Boston, MA: Harvard University Press. Wigfield, A. (1994). Expectan cy-Value theory of achieveme nt motivation: a development perspective. Educational Psychology Review, 6, 49. Wigfield, A., Eccles, J. & Rodriguez, D. (1998) The development of childrens motivation in school contexts. In a. Iran-Nejad & P. D. Pear son (Eds.), Review of research in education, 23. Washington, DC: American Edu cational Research Association. Wilson, M. & Languis, M. (1990) A topographic study of differences in the P300 between introverts and extroverts. Brain Topography, 2, 369.

PAGE 98

98 BIOGRAPHICAL SKETCH Susan Davis earned her Bachelor of Science degree for psychology and her Master of Arts in Education for school psychology from the Univ ersity of Florida. Sh e received her doctorate of philosophy degree for school psychology at the Un iversity of Florida w ith specialization in counseling. Susan completed her internship at Sha nds at Vista, an inpatient psychiatric facility serving Florida youth in Gainesville Fl. Her primary field intere sts include severe behavioral and emotional disturbed children and adolescents. Her research interests include effects of individual characteristics and in terpersonal relationships within the learning environments on the academic and socioemotional well-being of children and adolescents. Additionally, Susan is interested in pursuing the link be tween emotional disturbance and students at risk for school drop out. Susan has presented at state and national conferences on the effects of classroom climate on motivation and achievement. She also has de signed and began implementation of a model for a school-home advocacy and information resour ce (SHAIR) network which advocates to aid parents in gaining a better unde rstanding of special educa tion needs and interventions.


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Title: Effects of Motivation, Preferred Learning Styles, and Perceptions of Classroom Climate on Achievement in Ninth and Tenth Grade Math Students
Physical Description: Mixed Material
Copyright Date: 2008

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EFFECTS OF MOTIVATION, PREFERRED LEARNING STYLES, AND PERCEPTIONS OF
CLASSROOM CLIMATE ON ACHIEVEMENT IN NINTH AND TENTH GRADE MATH
STUDENTS




















By

SUSAN E. DAVIS


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2007

































O 2007 Susan E. Davis

































To My Husband









ACKNOWLEDGMENTS

I would like to thank my chair and mentor, Dr. Thomas A. Oakland, whose guidance and

support have contributed greatly to my present achievement and to my future potential. I would

also like to thank my husband, Jack, and my son, Jim, whose love and support sustain me.

Finally, I would like to thank my mother, Eleanor, whose own success has been my inspiration.












TABLE OF CONTENTS


page


ACKNOWLEDGMENTS .............. ...............4.....


LIST OF TABLES .........._.... ...............7.._.._ ......


LIST OF FIGURES .............. ...............8.....


AB S TRAC T ......_ ................. ............_........9


CHAPTER


1 REVIEW OF THE LITERATURE ................. ...............10....._.._ ....


Classroom Climate............... ...............10

Learning Styles Preferences .............. ...............17....

Temperament ................. ...............17......... ......
Temperament Theories ..........._...__........ ...............19......
Hi ppocrate s................. ...............19........ .....
Immanuel Kant ................. ...............20..___.........
W illiam James .............. ...............20....
Wilhelm Wundt ........._._.... ...............20..._._.. ......
Carl Jung .............. ...............21....

Myers-Briggs ................. .......... ...............22.......
Student styles questionnaire ................. ...............23........... ....
M otivation............... ..... ............2

Expectancy-Value Theory .............. ...............30....
Attribution Theory ................. ...............3.. 1..............
Self-determination Theory............... ...............32.
Goal Achievement Theory .............. ...............32....
Goal Orientation Theory............... ...............33.

Aptitude and Achievement ................ ...............35........... ....
A ptitude. ............. ...............35.....
Academic aptitude ................. ...............36.................
Achievement ................. ...............36.................

Proposal .............. ...............38....
Hypotheses............... ...............3
Study One .............. ...............39....
Study Tw o .............. ...............41....

2 MATERIALS AND METHODS .............. ...............43....


Participants .............. ...............43....
Instrumentation ............. ...... ._ ...............44....
Classroom Climate .............. ...............44....

Learning Style Preferences ............. ...... ._ ...............45....












M otivation .............. ...............46....
Academic Aptitude ................. ...............46.................
Achievement ................. ...............47.................
Procedure .............. ...............47....


3 RE SULT S .............. ...............53....


Preliminary Analysis of Data ................. ...............53................
Gender Effects ................. ...............53.......... .....
Grade Level Effects ................. ............. ...............53 .....
Race/Ethnicity and Family Income Effects ................. ...............55........... ...
Teacher and Class Period Effects ................ ...............57........... ...

Study O ne .............. ............ .. ... .. ..................... .......5
Students' Ratings of Classroom Climate Will Predict Math Achievement ....................57
Students' Ratings of Motivation Will Predict Math Achievement ................. ...............58
Study Tw o............... .. ........ ... .. ... ... ... .. ... .............5
Students' Preferred Learning Styles Will Predict Math Achievement............................59
Contribution of a Confluence of Variables to Math Achievement beyond that
Obtained Individually .............. ...............60....

4 DI SCUS SSION ................. ...............62................


H ypotheses............... .. ...... ... ....... ........... .... ........6
Students' Ratings of Classroom Climate Will Predict Math Achievement ....................62
Students' Ratings of Motivation will Predict Math Achievement ................. ...............65
Students' Preferred Learning Styles will Predict Math Achievement.............................6
Contribution of a Confluence of Variables to Math Achievement Beyond that
Obtained Individually .............. ...............67....
Im plications .............. ...............69....

5 LIMITATIONS AND FUTURE STUDIES ......__....._.__._ ......._.. ............7


APPENDIX


A FORM S ........._.___..... ._ __ ...............77.....


B MI SCELLANEOUS STABLE S ............ ..... .__ ............... 0...


LI ST OF REFERENCE S ............ ..... ._ ............... 1....


BIOGRAPHICAL -SKETCH .............. ...............98....










LIST OF TABLES


Table page

1-1 Scales of the What Is Happening in This Class ................. ...............15.............

1-2 Jungian typologies .............. ...............21....

2-1 Participant demographic information .............. ...............43....

3-1 Gender differences ................. ...............53........... ....

3-2 Means and Standard Deviations by Grade ................. ...............54........... ..

3-3 Tests for effects between groups by race/ethnicity and income .............. ...................56

3-4 Tests for effects between groups by parent education ................. ......... ................56

3-5 Tests for effects between groups by teacher and class period .............. .....................5

3-6 Classroom climate full model for prediction of achievement ................. ............... .....58

3-7 Classroom climate final model for the prediction of achievement ................. ................58

3-8 Motivation full model for the prediction of achievement ................. ................ ...._.58

3-9 Motivation final model for the prediction of achievement ...........__ ... ....__...........59

3-10 Preferred learning styles full model for the prediction of achievement. ................... .........59

3-11 Preferred learning styles final model for the prediction of achievement .........................60

3-12 Unique contribution of variables to the predication of achievement ............... ..............60

3-13 Full model of confluence of variables for the prediction of achievement ................... ......61

3-14 Final model of confluence of variables for the prediction of achievement ................... ....61

6-1 Descriptive statistics for full model of variables ................. ...............80..............

6-2 Means and standard deviations for final model of confluence of variables ................... ...80










LIST OF FIGURES


Figure page

1-1 Relationship of constructs examined in this study ................. ...............39..............

1-2 Relationships between classroom climate domains with math achievement. ................... .40

1-3 Contribution of classroom climate, motivation, and preferred learning styles to math
achieve ent. ............. ...............40.....









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

EFFECTS OF MOTIVATION, PREFERRED LEARNING STYLES, AND
PERCEPTIONS OF CLASSROOM CLIMATE ON ACHIEVEMENT IN NINTH AND TENTH
GRADE MATH STUDENTS

By

Susan E. Davis

May 2007

Chair: Thomas A. Oakland
Major: School Psychology

One hundred three ninth and tenth grade algebra students completed self-reports of

motivation, classroom climate, and learning styles preferences. A nonverbal measure of aptitude

and an algebra pretest was administered at the beginning of the academic year (August, 2007)

and an algebra post test was administered at the midpoint of the academic year (February, 2007).

Results indicated self-reported levels of motivation were not significant predictors for

achievement in algebra class. However, for classroom climate, students with lower ratings for

classroom involvement and higher ratings of task orientation demonstrated higher increases in

achievement than students with higher ratings of involvement and lower ratings of task

orientation. Additionally, students displaying a thinking preference achieved high scores than

student with demonstrating a feeling preference. Results of this study indicate students whose

perceptions and preferences are more consistent with instructional style demonstrate higher short

term gains in math than students with less congruent preferences and perceptions.









CHAPTER 1
REVIEW OF THE LITERATURE

Each individual brings into the classroom his or her own educational history and personal

characteristics. These characteristics include one's sense ofwell-being, self-efficacy beliefs

(House, 2002; Jackson, 2002), self-concept (Crohn, 1983), sense of belongingness (Goodenow,

1995; Osterman, 2000), satisfaction with social activities (Townsend & Hicks, 1997),

interpersonal relationships (Osterman, 2000), and preferred learning styles (Lawrence, 1982).

Learning style preferences refer to those methods a child uses when receiving and processing

information. These methods contribute to defining individual-environment interactions. Thus,

students' preferred learning styles may affect perceptions of their learning environment, levels of

motivation to engage, and their achievement.

This study examines the impact of students' preferred learning styles, perceptions of

classroom climate, and self-reported levels of motivation as they relate to academic achievement

in tenth grade students. Compared to students reporting discrepancies between preferred

learning styles and instructional methods, students with a better student-environment fit are

expected to report more positive perceptions of classroom climate and higher levels of

achievement motivation, resulting in higher levels of academic achievement.

Classroom Climate

Student achievement is influenced by feelings of belongingness to their school

environment (Deci, Vallerand, Pelletier, & Ryan, 1991; Osterman, 2000)). School

belongingness refers to feelings of being accepted and valued by their peers (Wilms, 2003a),

whilst the participation component "is characterized by factors such as school and class

attendance, being prepared for class," and completing assignments (Wilms, 2003a). Those who

report a higher feeling of belongingness with others display lower rates of emotional distress,










drug abuse, violent behavior, criminal behavior, suicide, and school dropout rates (Battistich &

Horn, 1997; Chipuer, Bramston, & Petty, 2003; Osterman, 2000; Resnick et al., 1997). Schools

that promote belongingness, safety, security, morale, and parental and community involvement

also promote lower drop out rates, higher attendance, greater levels of student engagement and

motivation (Goodenow, 1993), school effort and involvement (Anderman & Anderman, 1999),

positive affect (Anderman, 1999) and improved educational outcomes (Alspaugh, 1998; Chipuer,

Bramston, & Petty, 2003; Wilms, J., 2003; Robinson & Carrington, 2002; Rhodes & Houghton-

Hill, 2000; Wilms, 2003b). Children who feel involved within their school community are more

likely to report a stronger sense of identity and autonomy, higher self-regulation, respect for

authority, and a lower propensity to engage in deviant or negative behavior (Johnson, Lutzow,

Strothoff, & Zannis, 1995; Kunc, 1992; Osterman, 2000; Chipuer, Bramston, & Petty, 2003).

Thus, the extent to which a school community promotes belongingness affects student

development, motivation, and achievement (Kunc, 1992; Osterman, 2000; Wilms, 2003a).

The degree to which schools adopt policies and support practices designed to promote

student engagement is measured through perceptions of school climate. School climate refers to

the psychosocial, academic, organizational, and cultural factors that comprise an education

environment (Stringfield, 1994; Walberg, 1979) and are thought to influence student outcomes

(Creemers, 1994; Fernandez, Cannon, & Chokshi, 2003; Stewart & Brendefur, 2005). School

climate indirectly affects classroom climate through the adopted policies and practices of the

teachers (Ellis, 1996; Robinson & Carrington, 2002; Wilms, 2003b). Teacher practices,

expectations (Crohn, 1983), creativity (Denny & Turner, 1969), and student interactions

(Jacobson, 2000; Juarez, 2000; Wang et. al., 1993) help influence students' perceptions of their

classroom climate.









Classroom climate encompasses all dimensions of classroom life (Wang, Haertel, &

Walberg, 1993). The physical arrangement of furniture, availability of resource materials, length

of class period (Chapin & Eastman, 1996), level of task difficulty, type and pace of instruction

(Wang et al., 1993), predictability of the environment (Anderson, Stevens, Prawat, & Nickerson,

1987), and the value placed on interpersonal relationships (Gottfredson & Gottfredson, 1989)

influence classroom climate. Positive classroom climates are safe and supportive and provide

ample opportunities for exploration and experimentation.

Student perceptions of classroom climate are guided, in part, by individual values and

expectations for success (Eccles, 1983). Classroom climates that promote individual goal setting

and provide choices for students are preferred by adolescents (Pintrich et al., 1994). Classrooms

that value effort and foster positive feelings toward learning promote mastery of subj ect material

(Ames & Archer, 1987; Gaith, 2003). Clear and structured rules (Keyser & Barling, 1981),

focused, organized, and well planned lessons (Proctor, 1984), relevant curricula (Townsend &

Hicks, 1995), explicit learning objectives, guided student practice, frequent assessment, and

positive feedback (Wang et al., 1993) promote deeper approaches to learning, resulting in higher

achievement outcomes (Alspaugh, 1998; Dart, Burnett, & Purdie, 2000; Haertel et al., 1981;

McRobbie &Fraser, 1993).

Associations between climate and outcome are inconsistent (Davis, Davis, & Smith, 2004).

Although more positive ratings of classroom climate were associated with higher levels of math

achievement (Davis, 2004; Davis, Davis, & Smith, 2004; Goh & Fraser, 1995), no associations

were found for reading, science, or social studies. Studies of gender differences indicate girls

tend to rate classroom climate more favorably than boys (Townsend & Hicks, 1997).










Cooperative goal structured classroom environments include those which promote group

collaboration, performance and achievement, versus competitive goal structured environments,

which promote individual performance and achievement. Although cooperative learning was

found to increase academic success for females (Gardner, Mason, & Matyas, 1989), Gaith (2003)

found both males and females benefited. However, gender differences in classroom climate

ratings or achievement were not evident in a study of middle school students (Davis, 2004;

Davis, Davis, & Smith, 2004).

Gender perceptions of teacher support indicate males tend to feel they are treated more

sternly than females, yet believe teachers have higher expectations for girls in academic

performance (Myhill & Jones, 2006). Additionally, both boys and girls reported feeling that

female teachers were more likely to treat both genders more fairly than male teachers.

Teacher perceptions of students may impact student perceptions of classroom climate. For

example, in a study of ethnically diverse classrooms, teachers tended to develop better

relationships with students with similar ethnic backgrounds (Saft & Pianta, 2001). White

teachers tended to underestimate aptitude and predictions of achievement for African American

youth (Richman, Boelsky, Koovand, Vacca, & West, 1997), call on and praise white youth

compared to African American youth, and more likely to aid White students through the giving

of clues for partial responses (Casteel, 1998). Thus, racial/ethnic factors may influence minority

students' ratings of classroom climate for teacher support, cooperation and cohesiveness, as well

as perceptions of involvement and equity.

The assessment of classroom climate generally focuses on student interest and

participation, interclassroom relationships, support within the environment, emphasis on task

completion, perceived task difficulty, interclassroom competition, clarity and enforcement of









rules and expectations, and the overall environmental organization and management (Moos,

1974). According to Moos, climate refers to a group's impression of the social and

psychological atmosphere of any social setting. The use of the Classroom Environmental Scale

(CES) (Moos, 1974) allows students to rate the social climate along four dimensions:

relationships, personal development, system maintenance, and system change. Relationships

refer to the types and intensity of relationships, including those between teacher-student, student-

student, and staff-staff. They reflect the extent to which individuals within the environment are

involved, helpful, open, and supportive. Personal development includes competition that

emphasizes academic achievement and direction of personal growth and self-enhancement.

System maintenance refers to organization and orderliness, including clarity and consistency of

classroom rules and teacher consistency. System change refers to the manner and facility of

change within the classroom, and the variety and creativity of classroom activities.

The CES was designed to measure relevant aspects of conventional classroom

environment. The Individualized Classroom Environment Inventory (ICEQ) (Fraser, 1986) was

developed to measure qualities that help differentiate conventional classrooms from

individualized settings. Although both instruments are useful in measuring different aspects of

classroom climate, no one instrument included a comprehensive measure of classroom climate.

The What Is Happening in This Class (WIHIC; Fraser, Fisher, & McRobbie, 1996;

Aldridge & Fraser, 2000; Fraser, 1998) is one of the most recent and widely used learning

environment instruments. The WIHIC was selected due to the breadth of the measure across

several qualities of classroom climate. The WIHIC measures students' perception of their

learning environment. The measure corresponds to other measures selected for this study in










which students' perceptions of their environment is examined in relation to achievement, rather

than actual or unbiased measures of the classroom or classroom tasks.

The WIHIC is designed for use with secondary classrooms and combines the most relevant

scales from existing questionnaires. The WIHIC consists of 7 scales and 56 items scored using a

five-point Likert scale. The seven scales include measures of student cohesiveness, teacher

support, involvement, investigation, task orientation, cooperation and equity (Table 1-1).

Table 1-1. Scales of the What Is Happening. in This Class
Student Cohesivenss The extent to which students are friendly and supportive
of each other
Teacher support The extent to which the teacher helps, befriends, and is
interested in students.
Involvement The extent to which students have attentive interest,
participate in class, and are involved with other students
in assessing the viability of new ideas.
Investigation The extent to which classes emphasize skill building,
inquiry, and their use in problem-solving and
investigation.
Task orientation The extent to which completing planned activities and
saing on the subject matter are important
Cooperation The extent to which students cooperate with each other
during activities
Equity The extent to which the teacher treats students equally,
including distributing praise, questions, and opportunities
to be included in discussions

Student cohesive refers to the extent to which students are friendly and supportive of each

other. An example of an item that assesses student cohesiveness is "I make friends among

students in this class". Teacher support refers to the extent to which teachers demonstrate

interest in student success. An example of an item that assesses teacher support is "The teacher

takes a personal interest in me." Student involvement refers to the extent to which students

contribute to class discussion. An example of an item that assesses involvement is "I discuss my

ideas in class". Investigation refers to the extent to which students seek solutions to problems.

An example of an item that assesses investigation is "I carry out investigations to test my ideas."










Task orientation refers to the extent to which students value task completion. An example of an

item that assesses task orientation is "Getting a certain amount of work done is important."

Cooperation refers to the extent to which students are involved with peers when completing

tasks. An example of an item that assesses cooperation is "I cooperate with other students when

doing assignment work". Equity refers to the extent to which students perceive their

environment as fair and equitable. An example of an item that assesses equity is "The teacher

gives as much attention to my questions as to other students' questions."

Ratings of classroom climate involve the subj ective perceptions of students of their

learning environment. Additionally, some scales can be used to compare perceptions with actual

or preferred classroom characteristics. Studies demonstrated a strong correlation between

perceptions of climate and the actual climate of the classroom (Allen & Fraser, 2002; Chionh &

Fraser, 1998; Hunus & Fraser, 1997). However, this study examines individual differences in

perceptions as they relate to achievement.

Racial/ethnic and gender differences may factor into ratings of perceived classroom

climate. In a study by Kim, Fraser, and Fisher (2000), Korean students were assessed regarding

their perceptions of the classroom climate and teacher behavior. On all seven scales, boys' and

girls' perceptions of the learning environment differed. Boys tended to rate Teacher Support,

Involvement, Investigation, Task Orientation, and Equity more positively than did girls.

However, cultural differences, such as perception of teacher authority in Korea, make cross-

cultural comparisons difficult (Fisher & Rickards, 1998). For example, in cross-cultural studies

across Autstralia and Taiwan found higher ratings of classroom climate for Australians on

Involvement, Investigation, Task Orientation, Cooperation, and Equity (Fraser and Aldridge,

1998).










Cultural differences also were found for teachers. Khine, M., & Fisher, D., (2001) found

that teachers created different types of learning environment based upon the values of their

culture. For example, while Asian cultures, such as Taiwan, value academic ability as the focus

of education, teachers in Australia considered academic ability to be one of many aspects

important to the learning experience. Socio-emotional development, considered important to

Australian schools were considered more of a family responsibility by the Taiwanese (Khine &

Fisher). Differences between Chinese and American expectations for teachers were also noted.

Chinese teachers were more likely to prize enthusiasm and clarity in instructional styles while

their American counterparts valued sensitivity and patience (Steven & Stigler, 1992). Thus,

these differences will be examined in this study. However, as this study compares individual

climate perceptions to individual achievement gains, no significant differences are expected.

Not only are elements in the classroom suggested to influence perceptions of classroom

climate, how students perceive their external world is influenced by temperament qualities.

Learning Styles Preferences

Temperament

Temperament refers to the consistent, enduring predispositions toward perspectives,

preferences, affect, behavioral patterns, and environment interactions from which an individual

approaches his environment (Benson, 2005; Joyce, 2000; Kristal, 2005). Innate and stable

(Goldsmith, Buss, Plomin, Rothbart, Thomas, Chess, Hinde, & McCall, 1987) temperament

traits account for variance in mood, level of activity, and emotional response early in life (Chess

& Thomas, 1996) and beyond (Thomas, Chess, & Birch, 1968).

Individual differences in temperament are present early in life are believed to be

biologically rooted and stable (Bates, 1989; Buss & Plomin, 1984). Temperament differences

have been examined through early patterns of brain electrical activity. For example, infants









demonstrating high motor activity and high negative affect display greater activation in the right

frontal region of the cortex. Infants demonstrating high motor activity and high positive affect

display greater activation in the left frontal region of the cortex (Calkins & Fox, 1992). Patterns

of brain electrical activity and patterns of behavior are consistent in infancy (Calkins & Fox,

1992).

Temperament traits have been found to have biological basis in brain structure and

neurotransmittier levels. For example, temperament traits associated with a propensity for

depression have been linked to a short version of a gene's protein, known as the serotonin

transporter protein. The short version is suggested to promote intense serotonin activity, thus

degrading connections in the mood-regulation system (Bower, 2005). This indicates poorer

communication response between cingulate activity and the amygdala. The amygdala regulates

negative emotions such as fear responses. Individuals with poor regulatory control of the

amygdala by the cingulate activity demonstrate higher levels of anxiety and increased sensitivity

to negative environmental stimuli, such as stressful events.

Other theories indicate differences in brain functioning are temperament related. For

example, Tomarken, Davidson, Wheeler, & Kinney (1992) found increased right prefrontal

region activation was associated with an increased disposition for negative affect. Extreme right

frontal electroencephalographic activity associated with higher cortisol levels were indicated in

monkeys with increased fearful response compared with monkeys with extreme left frontal

activity (Kalin, Larson, Shelton, Davidson, 1998). Studies linking temperament to biological

dispositions are increasing. A method known as quantitative trait loci (QTL) analysis enables

the location and identification of chromosomal regions involved in trait variability (Mormede,

Courvoisier, Ramos, Marissal-Arvy, Ousova, Desautes, Duclos, Chaouloff, and Moisan, 2002).










Temperament traits evolve as a result of developmental and environmental influence to

comprise complex behavioral styles (Thomas et.al., 1968). For example, some children

demonstrate low sensitivity and react readily to low intensity environmental cues. They may

appear to be overwhelmed easily. Other children demonstrate high sensitivity to environmental

cues. They may appear more unaware, unobservant, or unconcerned with environmental cues.

Thomas and colleagues (1968) describe three categories of infant temperament as easy,

difficult, and slow- to- warm- up. Easy temperament describes traits that are flexible, adaptable,

and sociable. Slow to warm up infants are described as slow to adapt to changes in environment,

lower in activity level, somewhat hesitant with unfamiliar people or places. Difficult

temperament describes traits that are resistant to transitions and changes, withdrawn from

unfamiliar environments, difficult to console, and intense negative mood. Although many

infants are readily categorized within the three trait paradigm, some infants demonstrate an

interaction of these three.

Categorization of temperament as inborn traits is not new. For centuries, man has

attempted to classify, characterize, and explain human behavior through temperament. Current

efforts to describe and quantify temperament qualities have their roots in early theories of

temperament.

Temperament Theories

Hippocrates

Some of the earliest theories developed in ancient Greece attempted to explain behavior

through temperament styles that reflect individuals' dispositions and perspectives. Temperament

styles were thought to effect how one gathers, processes, and responds to stimulus. For example,

Hippocrates, considered the father of medicine, theorized that the balance of four humors (i.e.

blood, yellow bile, black bile, and phlegm) forms the basis for disease, health, and personality










(Berens, 2000). A balance of the warm and cool and dry and moist provides both an ideal bodily

quality and perfect personality qualities. Lesser qualities arise from an imbalance, or dominance,

of warm and cool versus dry and moist. Those who are sanguine have blood dominance, cool

and dry bodily qualities, and are cheerful, confident, and optimistic. Those who are melancholy

have black bile dominance, warm and dry bodily qualities, and are depressed, melancholic, or

unhappy. Those who are choleric have yellow bile dominance and are easily angered and bad

tempered. Finally, those who are phlegmatic have phlegm dominance and are calm, sluggish,

and unemotional. These four humors serve as the basis for later interpretation of temperament.

Immanuel Kant

Immanuel Kant, an 18th century Prussian philosopher, believed the four humors provided a

basis for temperament (Ferrar, 1994). According to Kant, temperament refers to the energetic

and emotional characteristics of behavior. Sanguine depicts a sociable and carefree

temperament. The melancholic depicts an anxious and unhappy temperament. The choleric

depicts an irritable temperament. The phlegmatic depicts a reasonable and persistent

temperament.

William James

William James later categorized temperament into two maj or types: tough-minded and

tender-minded (Altorf, 2005). Tough-minded temperaments are empiricists, materialistic, and

sensing; tender-minded are rationalistic, idealistic, or imaginative.

Wilhelm Wundt

In the early 20th century, Wilhelm Wundt categorized temperaments into a quantitative

two-dimensional system: energetic and emotional. Based on four temperament typologies, the

energetic and emotional are classified as strong or weak and changeable or unchangeable

(Thayer, 1996). This theory of opposing forces in temperament was adopted by Carl Jung.









Carl Jung

Attitude types describe perceptional orientations that are either extroverted or introverted.

Those who are extroverted focus on the immediate, objective, external world. They appear to be

open, sociable, and amiable. They enjoy participating in social events and readily interact with

others. In contrast, those who are introverted focus internally and respond subjectively to the

external world. Preferring meditation and reflection to socialization, they tend to have a calm

outward appearance and may be perceived as closed, withdrawn, and aloof.

Jung believed two basic perceptual functions, rational and irrational, direct how we

perceive information and make decisions. Rational perceptual functional types are represented

through the dichotomous qualities of thinking versus feeling and sensing versus intuitive. Those

with a thinking functioning type are guided by reason and rely on logical approaches when

drawing conclusions and making decisions. In contrast, those with a feeling functioning type

base decisions upon subj ective and affective considerations.

Irrational perceptual functioning types are based upon the intensity of perception. Those

with a sensing perceptual functioning type analyze information processed through the senses and

are oriented to the objective, real world. In contrast, those with an intuitive perceptual

functioning type rely upon the subj ective, instinctual, and indirect perception of ideas.

Although each individual uses all four functions in their lives, they do so at variable levels,

with variable frequency, and with variable levels of success. Jung proposed individual

preferences comprise dominant functions, extrovert or introvert, and are supported by auxiliary

perceptual functions. Based upon these typologies, Jung proposed eight personality types.










The j oining of attitude and functioning produces eight psychological types. Those with an

extroverted thinking type are logical, objective, and fact oriented. Those with an extroverted

feeling type are guided by feelings. Those with an extroverted sensing type are reality based and

prefer tangible, perceptible things in life that appeal to the excitation of sensation. Those with an

extroverted intuitive type are insightful, perceiving the imperceptible associations of events,

objects, people, and ideas.

Those with an introverted thinking type tend to value their own creative ideas and theories

to others'. They are creative, insightful, and prefer solitude and self-reflection. Those with an

introverted feeling type prefer ideas, moods, and intangible feelings. They appear outwardly

pleasant and sympathetic yet prefer to remain impartial and avoid influencing others. Those with

an introverted sensing type juxtapose their subj ective interpretation to obj ects, events, or people,

altering perception of obj ective reality. Those with an introverted intuitive type are guided by

the collective consciousness and are able to grasp the inner images or essence of external obj ects.

Myers-Briggs

Expanding upon Jung' s typologies, Isabel Myers and Kathryn Briggs (1962) believed a

fourth dimension of functioning related to individual preferences for lifestyle organization.

Those with a judging type are organized, preferring to manage themselves and the external

world. Those with a perceiving type are flexible, preferring to experience the external world.

Myers and Briggs (1962) suggest individual functioning preferences can be arranged in 16

distinct types based upon extroversion-introversion, judging-perceiving, sensing-intuitive, and

thinking-feeling. Extroversion-introversion describe attitudes and orientations. Those with an

extroverted orientation derive their energy from the external world. They feel energized when

engaged with others. Conversely, those with an introverted orientation derive their energy from

internal thoughts, feelings, and ideas and tend to reenergize through quiet solitude and reflection.









The sensing-intuitive dichotomy describes a perception preference. Those with a sensing

preference are oriented to the present. They prefer tangible, objective information. Those with

an intuitive preference are future oriented. They prefer global, abstract, and imaginative

information.

The thinking-feeling dichotomy describes a decision-making style preference. Those with

a thinking preference use logic, objectivity, and analysis to arrive at decisions. Conversely, those

with a feeling preference use subj ective personal standards, seek the maintenance or creation of

harmony, and consider the potential impact of decisions.

The judging-perceiving dichotomy describe an environmental orientation preference.

Those with a judging preference use advanced planning and organization. They rely upon either

the thinking or feeling functions. Those with a perceiving preference are adaptable and less

structured. They rely upon the sensing or intuitive functions and enjoy flexibility and

adaptability.

Student styles questionnaire

The MBTI is designed to survey the temperament preferences of late adolescents and

adults. Based upon the Jungian theory and the work of Myers and Briggs, the Student Style

Questionnaire (SSQ) was selected for use in this study because it was designed to survey the

temperament preferences of children ages 6 through 17(SSQ; Oakland, Glutting, & Horton,

1996),. The SSQ incorporates four bipolar dimensions: extrovert/introvert,

practical/imaginative, thinking/feeling, and organized/flexible.

The Student Styles Questionnaire (SSQ; Oakland, Glutting, & Horton, 1996), a 69 item

self-report temperament scale, is designed to measure temperament preference and thus to

identify learning styles preferences of children ages 8 through 17.










The SSQ assesses four dichotomous temperament types: extroversion (E) /introversion (I),

practical (P) /imaginative (M), thinking (T) /feeling (F), and organized (0) /flexible (L).

Extroversion versus introversion. The extroversion and introversion dimensions refer to

the source from which children receive energy. Students with a preference for an extroverted

style generally derive energy from being with others, need considerable affirmation and

encouragement from others, prefer to have many friends, and tend to take on the characteristics

of those around them. They leamn best through talking and cooperative group activities. An

example of an item that assesses the extroversion dimension is "In school I prefer active work

groups".

Students with a preference for an introverted style generally derive their energy from

themselves. They prefer to have a few close friends, have a few well-developed interests, and

enjoy spending time alone. They are inclined to be hesitant to share their ideas with others. They

appreciate acknowledgement of their careful work and reflection. They learn best by having

time to think about and reflect upon what they have learned. An example of an item that

assesses the introversion dimension is "In school I prefer quiet seatwork".

Practical versus imaginative. The practical and imaginative dimensions refer to

perceptions of the environment. Students with a preference for a practical style focus their

attention on what is seen, heard, or experienced through their other senses. Students with this

preference often base their decisions on facts and personal experience. They often learn best

using step-by-step approaches, are provided with many examples and hands-on experience, and

view what they are learning as applicable to their lives. They become discouraged when work

seems complex. An example of an item that assesses the practical dimension is "In school I like

to leamn about facts that help me know lots of things".









Students with a preference for an imaginative style prefer theories to facts and focus their

attention on generalizations and global concepts. They often base their decisions on intuitive

hunches, and may overlook details when learning or doing work. They leamn best when given

opportunities to use their imagination and contribute their unique ideas. They appreciate others

who value and praise their creativity. An example of an item that assesses the imaginative

dimension is "In school I like to learn about ideas that make me think in new ways".

Thinking versus feeling. The thinking and feeling dimensions refer to the manner in

which children make decisions. Students with a preference for a thinking style rely on obj ective

and logical standards when making decisions. They want to be treated fairly and desire truth to

be told accurately. Further, because they highly value the truth, they may tell others unpleasant

things in a blunt fashion and may hurt others' feelings in the process. They may praise others

infrequently and may be uncomfortable openly expressing their emotions or feelings. These

students tend to enj oy competitive activities and learn best when information presented is

logically organized. An example of an item that assesses the thinking dimension is "I decide

about things based on what' s in my head".

Students with a feeling style tend to rely on their feelings and own subj ective standards

when making decisions. They generally are compassionate and sensitive to the feelings of

others, and value harmony. Students with a feeling style tend to leamn best when engaged in

cooperative activities that help personalize their learning. An example of an item that assesses

the feeling dimension is "I decide about things based on what's in my heart".

Organized versus flexible. The organized and flexible dimensions refer to the propensity

of children to either make decisions promptly or delay them. Students who prefer an organized

style like to make decisions as soon as possible and prefer structure and organization. They do









not cope well with surprises or changes to their routine. They like to rely on lists and are likely

to respond well to a more structured and organized setting. Expectations others have for them

should be communicated clearly and schedules clearly established and followed. Students with

this style like to do things the right way and enj oy receiving praise for completing work in a

timely manner. An example of an item that assesses the organized dimension is "I like my desk

to be clean and orderly".

Students who prefer a flexible style delay decision-making as long as possible and feel that

they never have sufficient information to make decisions. They prefer a flexible, open schedule,

enj oy surprises, and adapt well to new situations. They may not respond well to externally

imposed rules and regulations. The manner in which they learn best is somewhat complex.

They are most highly motivated when given some flexibility in their assignments and are able to

turn work into play. However, teachers and parents may have to provide structure and assist

them in other ways to complete assignments on time. An example of an item that assesses the

flexible dimension is "I like my desk to be any old way".

Nature versus Nurture

Longitudinal and physiological studies have noted biological basis for differences in

extroversion-introversion. Longitudinal and physiological studies have noted biological basis for

differences in extroversion-introversion. For example, increased in cortical blood flow (Wilson

& Languis, 1990) and levels of anterior temporal lobe activity (Sternberg, 1990) has been

associated with introversion compared to extroversion. Joyce & Oakland (2005) posit the lower

activity levels may be associated with the tendency of extroverts to seek external reinforcement

from their environment and the need for introverts to withdraw from others in order to










rejuvenate. Other differences noted between introverts and extroverts include increased risks for

hypertension and heart disease for extroverted individuals (Shelton, 1996).

Biological differences were also noted in those with practical versus imaginative

temperaments. Greater left hemispheric activity has been associated with the practical

temperament. Conversely, greater right hemispheric activity has been associated with the

imaginative temperament (Newman, 1985). Increased risk of heart disease and hypertension also

were associated with practical temperament (Shelton, 1996).

In addition to biological differences, differences in the environment also are noted. Some

of the factors influencing temperament styles are purported to include family values (Rowe &

Plomin, 1981), family size (Rosenkrantz, Vogel, Bee, Broverman, & Broverman, 1968),

dissonance between environment and temperament styles, perceived self competence (Myers &

McCaulley, 1985), gender, race, and ethnicity (Myers, & McCaulley, 1985, Whiting & Whiting,

1975), intelligence (Levy, Murphy, & Carlson, 1972), and stages of development (Bassett, 2004;

Thayer, 1996). Cognitive and psychosocial development may strengthen or shift learning styles

preferences.

Cultural and development trends in the type and strength of leaming style preferences were

identified using the SSQ (Bassett, 2004; Thayer, 1996; Oakland, Alghorani, & Lee, 2007;

Oakland, & Lub, 2006).. The preference for extraverted learning style increases from age 8 to

13, then plateaus (Bassett, 2004). Young children tend to prefer thinking styles, with the

preference for feeling styles increasing during the late teen age years. Younger children

demonstrate a higher preference for organized styles while older children prefer more flexible

styles (Bassett, 2004; Thayer, 1996). Young children and older teenagers are most likely to

prefer imaginative styles (Bassett, 2004).










Gender and ethnicity also impact children's temperament. Males generally prefer thinking,

flexible, and practical styles. In contrast, females generally prefer feeling, organized, and

imaginative styles (Bassett, 2004; Thayer, 1996). Preference for thinking styles is relatively

stable for males. However, preference for feeling styles increases with age for females (Bassett,

2004). Although both young males and females exhibit a strong preference for organized styles,

a growing preference for flexible styles is seen in older males. However, a preference for

organized style remains relatively stable in females. Gender differences for practical versus

imaginative style were evident and inconsistent (Bassett, 2004).

Compared to Caucasian children, African American children demonstrate a higher

preference for a thinking style than a feeling style. Compared to Caucasian children, African

American and Hispanic children demonstrate a higher preference for practical and organized

styles than for flexible and imaginative styles (Stafford, 1994; Thayer, 1996).

Temperament based learning styles differences were noted on graduation rates, levels of

achievement, perceptions of teachers, academic persistence, and prevalence of gifted aptitude

(Schurr et.al., 1997; Myers et. al., 1998; Cornett, 1983; Oakland et. al., 2000).

Learning style preferences indicate how students gather and process information within

their environment. Because classroom practices reflect school policy, accepted pedagogy, and

teacher preferences for instructional methods, students may perceive their classroom

environment as either congruent or incongruent with their preferred learning styles. This, in

turn, may affect their perceived level of motivation to engage.

Motivation

Motivation refers to the incentive for goal directed behavior. Academic achievement

behavior refers to the demonstration of ability or competency in academic settings ( Machr &

Nicholls ,1980). Academic achievement motivation refers to the incentive to improve









educational performance, demonstrate educational mastery, or engage in educational tasks.

Academic achievement theories of motivation are disposition based or situation-specific.

(Brophy, 2001).

Disposition based theories assume motivation is innate, universal, stable, and

generalizeable (Brophy, 2001; Deci, 1991). Derived from personal beliefs and values (Epstein,

1994), motivation is tied to emotions, cognitions, and socialization. The affective qualities of

motivation (McClelland, 1987) that result from task engagement and completion (Ford, 1992),

include past experiences (McClelland, Kestner, & Weinberger, 1989), affective outcomes (e.g.

the need for esteem) (Dweck & Leggett, 1988), autonomy (Harter & Connel, 1984; Deci, &

Grolnick, 1995), competency, and mastery (Harter & Connel, 1984; Deci & Ryan, 2002) appear

early in a child's development. Individual values and expectations (Eccles, 1983), attributions

(Weiner, 1986), self-regulation, and self-efficacy beliefs (Hagen & Weinstein, 1995; House,

2002; Pintrich, & De Groot, 1990; Schunk, 1981; Urdan & Maeher, 1995) are important

determinants of academic achievement motivation levels. Motivation for learning is developed

through socialization within the home and school (Brophy, 2001).

Situation-specific theories of motivation assume extrinsic factors within the learning

environment (Ames, 1992; Freeman, 2004), including outcome expectancies, prior successes and

failures (Ames & Archer, 1987), perceived value for the outcome (Logon, 1968), and socio-

cultural contexts of goal attainment (Machr & Nicholls, 1980; Nicholls, 1992), interact with the

specific goals to elicit motivation for achievement behavior. External rewards (Madden, 1997),

concrete feedback (Bardwell, 1984), the reasonableness of goals (Hart, 1989), level of task

difficulty (Wigfield, 1994), performance versus mastery (Ames, 1992; Elliott & McGregor,

2001; Pintrich, 2000), and demonstration of competency (Dweck & Elliott, 1983; Dweck &










Leggett, 1991) are considered to be important determinants of levels of motivation for academic

achievement behavior.

Gender and racial differences may influence levels of academic motivation. For example,

in a study of middle school students, girls reported higher levels of motivation compared to boys,

seventh graders reported higher levels of motivation compared to eight graders, and African

American students reported higher levels of motivation than White students (Attaway, 2004).

Additionally, students who experience racial discrimination may exhibit declines in achievement

and perceived importance of tasks. However, students with strong ethnic support demonstrated

increased academic motivation when faced with adversarial climates (Eccles, Wong, & Peck,

2006).

Thus, academic achievement motivation, the drive to put forth persistent effort for

educational achievement, is influenced by the internal qualities of the student and the external

qualities of the learning environment (Ames, 1992; Davis, 2004; Davis, Davis, & Smith, 2004;

Ford, 1992; Heider, 1958). Theories of academic achievement motivation relative to these

internal and external qualities are reviewed below.

Expectancy-Value Theory

In expectancy-value theory, prior experiences are thought to play an important role

regarding students' motivation (Atkinson, 1980). Motivation for future task engagement

(Wigfield, 1994) and expectations for future success (Jacobson, 2000) are developed at an early

age (Wigfield, Eccles, & Rodriguez, 1994). Those who experienced failure, compared to those

who experienced success, are likely to participate less, report lower levels of motivation

(Jacobson, 2000), and are more likely to demonstrate academic avoidance, resulting in lower

academic achievement (Eccles, Wigfield, Midgely, Reuman, MacIver, & Feldlaufer, 1983).

Student motivation for engagement is influenced by the cost of engaging in a task, the perceived









usefulness of the task, and self-efficacy beliefs (Eccles, 1983). Mastery of subject material is

enhanced through positive attitudes for learning and valuing effort (Ames & Archer, 1987).

Attribution Theory

Students' attributions for academic success or failure are thought to be important

components of motivation (Weiner, 1986). Internal attributions for success and failure refer to

the internal assignment of responsibility for outcomes based upon competency beliefs. Internal

attributions for ability can be viewed as fixed or malleable. Compared to students who attribute

failure to innate deficiencies, those who attribute failure to poor effort are more likely to persist

with difficult tasks (Ames, 1983; Weiner & Kukla, 1970). Thus, attributions for performance

may be associated with level of effort, which is changeable, or due to innate ability, which may

be considered fixed.

External attributions assign outcome responsibility to uncontrollable factors within the

environment. For example, if a student receives high marks on an assignment, the student

attributes this to task ease. If a student receives low marks on an assignment, the student

attributes this to task difficulty. Rather than recognizing the importance of effort and ability, the

student perceived that task qualities determine his or her success. Compared to students with

external attributions, those with internal attributions for success are more likely to engage in

tasks that are challenging (Ames, 1983; Weiner & Kukla, 1970).

Based upon attribution theory, academic goals are thought to be task or ego involved.

Task involvement refers to the inherent value of learning and where success and failure are

attributed to effort. Attribution theory highlights the focus on the learning task and the strategies

required for mastery (Weiner & Kukla, 1970).

Ego-involvement tasks refer to students' internal attributions for success and failure. The

focus is on the self. Learning is perceives as a means to avoid appearing deficient.









Self-determination Theory

Motivation is influenced by self-esteem, (Dweck & Leggett, 1988), self-efficacy,

(Anderson, 2002), and self-determination (Ryan & Deci, 2000; Eccles, 1983). Self- esteem

refers to feelings of self worth (Dweck & Leggett, 1988). Self-efficacy refers to one's beliefs of

competency and capability (Anderson, 2002). Self-determination refers one's sense of autonomy

(Ryan & Deci, 2000). Individual differences for approaching success or avoiding failure are

associated with self-efficacy beliefs about the plausibility of goal attainment and affective

outcomes associated with success or failure (Ford, 1992). A sense of individuality, superiority,

and self-determination serve to motivate purposeful behavior.

Self-determination theory of motivation asserts the need to feel related, competent, and

autonomous promotes intrinsic motivation (Ryan & Deci, 2000). Thus, students are more likely

to engage in academic tasks when they feel a sense of affiliation within the classroom, of

personal initiative, and that they have the knowledge and skills needed to achieve academically

(Deci & Ryan, 2002; Kakman, 2004; Turner et.al., 1989).

Goal Achievement Theory

Goal achievement behavior is thought to be either mastery or performance based (Ames,

1992; Elliott & McGregor, 2001; Pintrich, 2000). Mastery goals are thought to involve intrinsic

learning goals and competency development. Performance goals are thought to focus on the

demonstration of one's ability (Dweck & Elliott, 1983; Dweck & Leggett, 1991). The nature of

the task provides the directive for either mastery or mastery performance approached. For

example, while the demonstration of reading comprehension may be mastery based, the

recitation of multiplication tables may be performance based.









Goal Orientation Theory

Compared to theories that are disposition based, goal orientation is thought to be situation-

specific (Nicholls, 1992), or based upon the specific characteristics of the task. Situation-

specific theories are associated with personal characteristics (Dweck, 1999), the nature of the

environment (Ames, 1992), the socio-cultural context (Machr & Nicholls, 1980), and social

status variables. Three orientations to goal achievement are based upon one's desire to

demonstrate high ability, to avoid the demonstration of low ability, or to develop competence

(Kaplan, 1992). According to this theory, the orientation determines how learners are guided in

their activities, thoughts, and feelings.

For example, mastery goals focus on promoting understanding, developing competence,

and improvement. Mastery oriented learners believe ability is malleable and based upon effort.

These learners tend to focus on competency and skill acquisition, are more likely to use cognitive

strategies, and are more likely to seek assistance when faced with difficult tasks (Dweck &

Legget, 1988). Perceptions of high self-efficacy are more likely to result in performance

approach. In contrast, perceptions of low self-efficacy are more likely to result in performance

avoidance (Kaplan, 2002).

If the learner believes ability is Eixed, static, and unalterable, their orientation is

performance based. Performance-avoidance goals involve a desire to avoid appearing

incompetent or less competent. The learner is more likely to lack strategies when faced with a

difficult task and may exhibit patterns of learned helplessness when faced with failure.

Performance-approach goals involve a desire to demonstrate competence. Learners seek to

gain positive judgments about their competency, tend to avoid challenging situations, and focus

on grades as a measure of performance.









The Motivated Strategies for Learning Questionnaire (Pintrich & DeGroot, 1990) was

designed to measure self-efficacy and intrinsic motivation. Based upon the expectancy-value

theory, MSLQ uses a 5-point Likert scale to assess students' perceptions about their expectations

based upon their performance, ability, task values, and task utility. The MSLQ has been found to

predict achievement in middle school mathematics (Davis, 2004; Davis, Davis, & Smith, 2004).

Although the expectancy-value theory is relevant to measures of academic motivation, it

does not account for an individual's orientation towards specific academic tasks. The Patterns of

Adaptive Learning Survey (PALS; Midgley, et al., 1996) was designed to measure mastery

performance, performance avoidance, and performance approach goal. Mastery performance

orientation refers to the extent to which students engage in academic tasks to promote

competence. An example of items reflecting the mastery performance is "It' s important to me

that I learn a lot of new concepts this year". Performance avoidance orientation refers to the

extent to which students desire to avoid demonstrating incompetence. An example of items

reflecting performance avoidance is "It' s important to me that I don't look stupid in math class".

Performance approach orientation refers to the extent to which students are interested in

demonstrating competence. An example of items reflecting performance approach is "It' s

important to me that other students in my math class think I am good at my class work".

The PALS was selected based upon the theoretical underpinnings that academic

achievement motivation is based upon the individual's perception of orientation toward the task

itself, rather than external motivational factors. This measure appeared to be consistent with

other measures selected in that the interest in individual perceptions of the environment are

considered. Additionally, the effects of gender and race/ethnicity will be examined. However,









since this study examines students' self-reported levels of motivation to their rate of achievement

gain, significant differences are not expected.

Aptitude and Achievement

Aptitude.

Aptitude, one's optimal ability for cognition, is generally measured through the use of

intelligence scales. The Wechsler Intelligence Scale for Children- Fourth Edition is one widely

used measure of intelligence. The WISC-IV is comprised of 15 subtests measuring verbal

comprehension, perceptual reasoning, processing speed, working memory, and full scale IQ.

The WISC-IV is designed for children ages 6 to 16. Administered individually, the 60-80

minute administration time suggested by Wechsler (2003) was found to be underestimated in a

study by Ryan, Glass, and Brown (2007). Thus the administration time precludes its use from

this study. Additionally, though measures of intelligence are designed to capture aptitude, the

role of prior experience, such as quality and level of education, English proficiency, and level of

vocabulary proficiency may depress IQ scores for individuals with minority status (Shuttleworth-

Edwards, Kemp, Rust, Hartman, & Radloff, 2004). For example on the Wechsler Adult

Intelligence Scales-Revised and Wechsler Intelligence Scales for Children-Third Edition,

differences by ethnicity were noted on the vocabulary subtests (Kaufman, McClean, & Reynolds,

1998; Paolo, Ward, Ryan, & Hilmer, 1996; Ardilo & Mareno, 2001). Additionally, block design,

a nonverbal subtest assessing perceptual reasoning abilities also reported significant differences

by race/ethnicity, unless educational experiences were controlled for (Kaufman, McClean, &

Reynolds, 1998; Overall & Levin, 1978; Paolo, Ward, Ryan, & Hilmer, 1996). When level and

qualityof education are controlled for, results from IQ tests tend to be more congruent between

ethnicities (Shuttleworth- Edwards, Kemp, Rust, Hartman, & Radloff, 2004).









According Hayes (1999), a strength of the MAT-SF is the lack of systematic bias against

gender or ethnic group. Thus, the MAT-SF was selected for its ease of administration, short

amount of time required for task completion, and consistency across gender and race/ethnicity.

The MAT-SF percentile ranks served as the controlled variable for aptitude.

Academic aptitude may impact rate of achievement gains. The Matrix Analogies Test-

short-form (MAT-SF; Naglieri, 1985) provides a relatively quick screening of one's aptitude

based upon four factors: reasoning by analogy, serial reasoning, pattern completion, and spatial

visualization. The MAT-SF provides a measure of visual conceptual ability, highly correlated

with cognitive processes involved in algebra.

Achievement

Achievement is measured as a gain in knowledge and skills. This study proposes and

examination of achievement in mathematics over the course of an academic semester in

secondary school algebra I classes. Research has demonstrated a gender trend, where boys

outperform girls in math in middle school and beyond (Chatterji, 2004; Chatterji,2005; Goh, &

Fraser, 1995; Spelke, 2005. However, developmental studies indicate similarity between males

and females in achieving mathematical milestones. For example, no gender differences were

noted in the acquisition of processes that involve recognizing geometric shapes, angles and

distance (Spelke, 2005), identifying landmarks in a visual-spatial relationship (Hespos & Rochat,

1997; Acredolo, 1978; Gouteux & Spelke; Riser, 1979), the ability to mentally rotate of objects

(Hespos & Rochat, 1997), orientation to geometrical objects (Hemner & Spelke, 1994; Hespos &

Rochat, 1997; Learmouth, Nadel, & Newcombe, 1999), understanding number word meanings

(Griffin & Case, 1996), and the development of spatial language (Hemner & Spelke, 1994).

While no gender differences have been associated with the development and acquisition of

primary mathematical abilities (Acredolo, 1978; Gouteux & Spelke, 2001; Riser, 1979; Herner &










Spelke, 1994; Hespos & Rochat, 1997; Learmouth, Nadel, & Newcombe, 1999; Spelke, 2005),

other studies suggest gender differences emerge and the math achievement gap widens during

the development of more complex processes associated with higher level quantitative reasoning

(Beilstein & Wilson, 2000). In tasks involving arithmetic calculation and remembering the

spatial location of objects, females outperform males. However, on tasks involving solving word

problem and remembering the geometric arrangement of the environment, males outperform

females (Halpern, 2000; Hyde, 2005).

Differences in the use of strategies for solving mathematical tasks also factor in the

performance level. Males tend to use the spatial relationships for obj ect comparison while

females focus on object features (Voyer, Voyer, & Bryden, 1995). Males continue to use spatial

imagery rather than verbal computation when solving word problems (Geary, Saults, Liu, &

Hoard, 2000). The differences in strategies impact performance on standardized tests, such as

the SAT-M, with males outperforming females (Gallagher & Kaufman, 2005).

Race/ethnicity has also been found to factor significantly in measures of math achievement

in standardized tests, such as FCAT performance. In 2004, FCAT statistics for math indicated

37% more Whites achieved a level 3 than did African American students and 20% more White

students achieved level 3 than did Hispanic students. Trends indicate all ethnicities are

improving in FCAT performance. However, consistent progress gains across race/ethnicity,

maintains the racial/ethnic achievement gap. Thus, although trends indicate the achievement

gaps have been closing in elementary grades the racial/ethnic achievement gap observed in

middle and high school have not changed significantly since 2000 (Chatterji, 2004;

Chatterj i,2005).










Gender and race/ethnicity have been demonstrated to factor in achievement levels in math

(Chatterji, 2004; Chatterji, 2005; Lee, 2002). However, in this study, achievement compares

individual performance on the sample FCAT test to their previous performance on the same test

administered at an earlier time. Thus, while comparisons across gender and ethnicity are

examined, they are not expected to significantly affect the results. The FCAT sample test was

selected in order to provide a standard sample of mathematical abilities across teachers and

classrooms.

Proposal

This proposed study will be conducted in three stages. The purpose of study 1 is to

analyze the effect of gender and race/ethnicity on achievement. Additionally, study one purports

to narrow the focus of variables that impact math achievement in order to increase the efficiency

and effectiveness of later work. The purpose of study 2 is to more directly test the dissertation' s

main hypotheses.

Hypotheses

Students respond to their learning environment, in part, as a result of personal, family,

teacher, and peer influences (Brophy, 2001; Deci, 1991). The manner in which students

perceive, approach, and respond to their learning environments is determined, in part, by their

learning style preferences (Bargar, & Hoover, 1984; Lawrence,1982; Oakland et.al. 1996;

Thayer, 1996; Thomas & Chess, 1968). Motivation and perceptions of classroom climate

influence achievement. (Davis, 2004; Davis, Davis, & Smith-Bonahue, 2004; Townsend &

Hicks, 1997; Urdan & Maeher, 1995).

Classroom climate is measured by seven variables, preferred learning styles by four, and

motivation by three. Inasmuch as there are fourteen independent variables, an attempt to

decrease their number would be advantageous to conducting subsequent statistical analyses.









Thus, the goal of study one is to determine relationships between classroom climate and math

achievement in order to identify classroom climate qualities that have the strongest impact on

math achievement.




AchievemetMotivation Temperament

Aptitude



Classroom
Climate
Perceptions


Figure 1-1. Relationship of constructs examined in this study

Study One

Study one is designed to investigate the following question: what are the relationships

between each of the seven domains of classroom climate and math achievement (Figure 2-2)?






































Figure 1-2. Relationships between classroom climate domains with math achievement.


Figure 1-3. Contribution of classroom climate, motivation, and preferred learning styles to math
achievement.

The following hypotheses are based upon the assumption that approximately three of the

seven classroom climate domains from the WIHIC will demonstrate a positive relationship with

math achievement (Dorman, 2003; 2004). Therefore, they will be referred to as variable 1,









variable 2, and variable number 3 at this stage in the study. Modifications in the hypotheses may

be made based upon actual knowledge of the relationships between seven classroom climate

domains and math achievement.

The study tests the assumption that classroom climate (Davis, 2004; Davis, Davis, &

Smith-Bonahue, 2004; Goh & Fraser, 1998), motivation (Davis, 2004; Davis, Davis, & Smith-

Bonahue, 2004), and preferred learning styles each contribute significantly to math achievement

(Figure 2-2). The study also tests the assumption that these three variables, in confluence,

contribute to math achievement beyond that obtained when each of the three variables is used

individually to predict math achievement (Figure 2-3).

Thus, this study examines relationships between classroom climate and math achievement,

by testing the following hypotheses:

* Three classroom climate variables will demonstrate a positive relationship with math
achievement, controlling for academic aptitude, gender, grade level, and ethnicity.

This study also examines relationships between motivation and achievement by testing the

following hypotheses:

* Mastery domain scores will demonstrate a positive relationship with math achievement,
controlling for academic aptitude, gender, grade level, and ethnicity.

* Performance avoidance scores will demonstrate a negative relationship with math
achievement, controlling for academic aptitude, gender, grade level, and ethnicity.

* Performance approach scores will demonstrate a positive relationship with math
achievement, controlling for academic aptitude, gender, grade level, and ethnicity.

Study Two

This study examines the relationships between learning styles preferences and math

achievement and examines the impact of the confluence of classroom climate, motivation, and

preferred learning styles on math achievement by exploring the following questions:










* What is the relationship between extroverted-introverted learning style preferences and
math achievement, controlling for academic aptitude, gender, grade level, and ethnicity?

* What is the relationship between practical-imaginative learning style preferences and math
achievement, controlling for academic aptitude, gender, grade level, and ethnicity?

* What is the relationship between thinking-feeling learning style preferences and math
achievement, controlling for academic aptitude, gender, grade level, and ethnicity?

* What is the relationship between organized-flexible learning style preferences and math
achievement, controlling for academic aptitude, gender, grade level, and ethnicity?

* Will the combination of knowledge of classroom climate, motivation, and preferred
learning styles will contribute to math achievement beyond that obtained when these three
variables are used individually to predict math achievement, controlling for academic
aptitude, gender, grade level, and ethnicity (Figure 2-3)?










CHAPTER 2
MATERIALS AND METHODS

Participants

One hundred three participants were selected from 150 ninth and tenth grade high school

mathematics students enrolled in an Algebra I class in a mid-size city public school in North

Central Florida. The high school population of 1895 students within the school district includes

Asians (3%), Hispanics (6%), African Americans (34%), and Caucasians (56%), of whom 48%

are male. The average teacher-student ratio reportedly is 1:22. Thirty-two percent of students

within the school are eligible for free or reduced lunch. The school graduation rate is 75%.

The participants included 81 ninth and 22 tenth grade math students enrolled in one of 5

Algebra I classes offered. One female teacher taught three algebra I classes, of which 2 were

morning classes and one was an afternoon class. One male teacher taught two afternoon classes.

The final sample included 49 males and 54 females of which 48.5% were Caucasian, 12.6% were

African American, 10.7% were Hispanic, and 2.9% were Pacific Islander-Asian, 5.8% were

multiracial, and 19% declined to identify themselves according to race/ethnicity. The income

ranged from below $20,000 per annum to above $65, 000 per annum. Of these, 22% declined to

respond, 27.2% believed the household income to range between $20,000 and $45, 000 annually,

25.2% believed the household income to range above $65,000 annually, 20.4% believed the

household income to range between $45,000 and $65,000 annually, and 5.8% believed the

household income to range below $20,000 annually.

Table 2-1. Participant demographic information
Race/Ethnicity Frequency Percent
Caucasian 50 48.5
African American 13 12.6
Asian/Pacific Islander 3 2.9
Hispanic 11 10.7
Multiracial 6 5.8










Table 2-1. Continued
Race/Ethnicity
South African

Below 20k
20k-45k
45k-65k
Above 65k
No response

Grade Level
9th Grade Students
10th Grade Students

Table 2-1
Gender
Female
Male
Mother's Education Level
High school
Some College
College Degree
Masters Degree or Above
Father' s Education Level
High school
Some College
College Degree
Masters Degree or Above


Frequency
1 qunc
Feueny
6
28
21
26
22

Frequency
81
22



Frequency
54
49

18
25
31
29

27
19
22
35


Percent

Percent
5.8
27.2
20.4
25.2
21.3

Percent
78.6
21.4


Percent
52.4
47.6

16.5
22.9
28.4
26.6

26.2
18.4
21.4
33.9

Instrumentation


Classroom Climate

The What Is Happening In This Class? (WIHIC) questionnaire was used to acquire data on

seven domains of classroom climate designed to predict learning outcomes (Fraser, Fisher, &

McRobbie, 1996). The WIHIC assesses the following domains: student cohesiveness, teacher

support, student involvement, investigation, task orientation, cooperation, and equity (Fraser,

Fisher, & McRobbie, 1996; Aldridge & Fraser, 1997).

Internal consistency estimates using Cronbach coefficient alpha are .81 for student

cohesiveness, .88 for teacher support, .84 for involvement, .88 for investigation, .88 for task

orientation, .89 for cooperation, and .93 for equity (Aldridge & Fraser 2000). Discriminant









validity coefficients of the seven domains of classroom climate ranged from .32 for student

cohesiveness to .49 for involvement in a cross-national study of Australian and Taiwanese

students (Aldrige, Fraser, & Huang, 1999). For facilitation in analysis, studies often reduce the

number of variables on the WIHIC to measure classroom climate (Allen & Fraser, 2002; Hunus

& Fraser, 1997; Khine & Fisher, 2001; Khoo & Fraser, 1997). For this study, three out of seven

variables will be selected based upon the strength of their correlation to achievement.

Learning Style Preferences

The Student Styles Questionnaire (SSQ; Oakland, Glutting, & Horton, 1996), a 69 item

self-report temperament scale, is designed to measure temperament preference and thus to

identify learning styles preferences of children ages 8 through 17. The SSQ assesses four

dichotomous temperament types: extroversion (E) /introversion (I), practical (P) /imaginative

(M), thinking (T) /feeling (F), and organized (0) /flexible (L). It was normed and

standardization on 7,609 students ranging in ages from 8 through 17, including 5547 Anglo

American, 1194 African American, and 868 Hispanic students. Students were selected from 61

school districts in 29 states plus Puerto Rico (Oakland, Glutting, & Horton, 1996).

Test-retest reliability coefficients derived over a 9 month period, ranged from .67 on the

practical-imaginative dimensions to .80 on the extroversion-introversion dimensions, with an

average test-retest reliability coefficient of .74. Studies indicate good convergent validity with

the Myers Briggs Temperament Inventory, good divergent validity with achievement and

intelligence, and good stability for persons who differ by age, gender, and race/ethnicity

(Oakland, Gutting, & Horton, 1996; Oakland, Glutting, and Stafford, 1996; Stafford & Oakland,

1996).









Motivation

The Patterns of Adaptive Learning Survey (PALS; Midgley, et al., 1996) was designed to

measure mastery performance, performance avoidance, and performance approach goal. Other

qualities include measures of academic self-efficacy and self-handicapping strategies. The

PALS consists of 56 items and uses a 5 point Likert response scale.

Internal consistency estimates are reported to be .86 for mastery performance scales, 75 for

performance avoidance scales, and .86 for performance approach scales (Midgely, 2002).

Studies indicate good convergent validity with other measures of motivation, good construct

validity, and good stability for PALS (Lipman & Moore, 2005; Anderman, Urdan, & Roeser,

2003).

Academic Aptitude

Academic aptitude was measured using the Matrix Analogies Test Short-Form (MAT-SF;

Naglieri, 1985). The MAT-SF is a 34 item group administered test of non-verbal reasoning

ability. The MAT-SF provides a relatively quick screening of one' s aptitude based upon four

factors: reasoning by analogy, serial reasoning, pattern completion, and spatial visualization.

The MAT-SF was normed and standardized on 4,468 students, grades kindergarten through 12,

representative of the 1980 U.S. Census for age, gender, ethnicity, geographic region, and

community size.

Internal consistency estimates range from .63 to .89. Test-retest reliability estimates range

from .51 to .91. The MAT-SF demonstrates good convergent validity with other measures of

intelligence, such as the Wechsler Intelligence Scale for Children- revised (Kamnes & McGinnis;

1994Slate, Graham, & Bower, 1996), Wechsler- Third Edition (WISC-III; Wechsler,

1991)(Hayes, 1999; Prewett, 1995), the Stanford Binet- Fourth Edition (Prewett, & Farhney,

1994), the Kaufman Brief Intelligence Test (Hayes, 1999; Prewett, 1995; Slate, Graham, &










Bower, 1996) and the Kaufman Test of Educational Achievement-Brief Form (Prewett &

Farhney, 1994).

Achievement

Achievement was measured as the change in values between scores on a sample FCAT test

administered at the beginning of the school year ( pretest data, August, 2006) and then

readministered at the midpoint of the year (posttest data, January, 2007). The FCAT sample

included two different tests, one for the ninth grade students and one for the tenth grade students

based upon a representative sample of math skills required for the expected level of mastery for

students in their corresponding grade. Raw scores for the ninth grade were converted to z-scores

in order to better represent the variance in population and to standardize comparisons across the

test versions. Raw scores were then converted to z-scores for the tenth grade test. Changes in

achievement were determined by subtracting the individual pretest z-scores from their posttest z-

scores.

Procedure

Approval for this proposed study was sought through the Institutional Review Board (IRB)

at the University of Florida (UF). The IRB was established in accordance with Federal

Regulations (45 CFR 46 and 21 CFR 56) and reviews research involving human subj ects under

the UF Federal Wide Assurance under the regulations promulgated by the U.S. Department of

Health and Human Services designed to safeguard the rights and welfare of human subj ects. A

copy of the research proposal and all questionnaires, surveys, and instruments was submitted for

review and approval.

Parental consent for survey participation was obtained for initial recruitment of each

student (Appendix A.3). A consent form describing the nature of the school-wide project, the

purpose of the study and the types of questions to be asked, as well as the task requirements and









time frame involved was composed then approved through the Institutional Review Board (IRB)

at the University of Florida (UF). Consent to participate was obtained from the parents or

guardians of the students. One week before the study began, 150 parent consent forms were

delivered to the participating math teachers to distribute to their students. Students were asked to

deliver the form to their parent to solicit consent. Parents were asked to sign the consent form to

indicate their agreement to allow their child to participate and return the form, with their child

the following day. Students returned their signed consent forms to their teacher. The consent

forms were collected from the teacher three days before the study and the day of the study.

Students who misplaced their forms were re-issued forms to take home to their parents. The

teachers reminded the students each day prior to data collection to return their consent forms.

Assent to participate was obtained from the students. An assent form was composed then

approved through the IRB (Appendix A.4). Students were given the assent form the first day of

the data collection. The study was orally described to the students, the assent form was

explained, and then students were asked to sign the form if they wished to participate.

Agreement to participate also was obtained from the principal and teachers during the

initial meeting to discuss the study and arrange for the testing to take place Students lacking

consent or assent were excluded from the study.

Students were asked to complete an F-CAT sample pre-test during class in August, 2006

consistent with their grade level. The tests were administered by the classroom teachers. The

tests were scored according the corresponding answer booklet given to the teacher. For three

classes, the tests were scored by the researcher. For two classes, the tests were scored by the

classroom teacher. All posttests were scored by the researcher. One problem associated with

geometry was eliminated from the tenth grade pre- and post-test. This allowed consistency in










amount of points scored across grades. The raw scores were converted to z-scores for

consistency in comparisons across grades, as well as to better represent the amount of variance in

a normally distributed population.

Students were asked to complete the SSQ, MAT-SF, WIHIC, PALS in December, 2006.

Each student was given a data packet consisting of the assent form, a demographic survey, and a

copy of each instrument. The demographic survey consisted of questions related to

race/ethnicity, estimated economic status of students' families, including income and education

level of parents, perceived value for math, overall math grade achieved the prior year, and

expectations for assigned midterm grade in math in the current academic year. Students were

asked to complete the survey as part of their survey packet. Students were advised they were not

required to complete any questions or items during the survey process if they did not wish to

disclose the information.

Each instrument, including the demographic survey data, was orally explained using a

copy of each as a visual aid. Students were allowed to complete the instruments in any order

they wished, with the exception of the aptitude test, which was administered in groups of ten

students with a twenty minute time limit.

During each data collection day, the researcher would review the requirements and was

available to the class for questions and comments. When students were finished, they raised

their hand. The researcher reviewed their surveys for incomplete or double scored items.

Students were asked to correct those items. Once completed, the surveys were returned to the

packet envelope and collected and marked according to grade level, teacher, and class period.

Students were reminded prior to each data collection that they could discontinue participation if










they wished, with no penalty. Students with incomplete data packets were later eliminated from

the study.

Students were asked to complete the math post-test in January, 2007. The same F-CAT

sample test was re-administered consistent with the first administration. Each teacher explained

the test and gave directions on completion. The teachers and the researcher were available for

questions and comments during the administration. Students were given calculators consistent

with the directions of the sample F-CAT administration. Students raised their hand when

finished. The tests were collected by the researcher and categorized by class period, teacher, and

grade level. The tests were scored consistent with the first administration, and the raw scores

were converted to z-scores. Math achievement was determined by subtracting the post-test z

score from the pretest z score.

Analysis: Data from the What is Happening in My Classroom Scale (WIHIC; Fraser,

Fisher, & McRobbie, 1996), the Patterns of Adaptive Learning Scales (PALS; Midgley, et al.,

1996), and Student Styles Questionnaire (SSQ; Oakland, Glutting, & Horton, 1996) were entered

into a text file and imported to SPSS (15.0 for Windows). An integrity check was performed to

ensure the accuracy of the data. Data files were sorted by descending order using the student

identification number with the order of variables listed in the same sort sequence. Initially, data

for climate, motivation, and grades were entered into imputed files. Data were merged by

identification number per each domain, matching cases listwise.

Using frequency and descriptive statistics, composite data were screened for missing

values, normality, and outliers. A composite score was computed using the WIHIC domains of

equity, student cohesiveness, teacher support, involvement, investigation, task orientation, and

cooperation. A composite score was computed using three PALS domains of mastery approach,










performance approach, and performance avoidance. Scores were reported on a continuum of

positive and negative values for the SSQ qualities of extroversion-introversion, practical-

imaginative, thinking-feeling, and organized-flexible.

A preliminary analysis, using an independent t-tests and one-way ANOVA, was conducted

to check for differences in gender, race/ethnicity, grade level, class period, and teacher. The

three independent variables (i.e. classroom climate, motivation, and preferred learning styles)

were examined in relation to the dependent variable of math achievement after controlling for

aptitude. Tests for gender, grade, and race/ethnicity, were conducted. Significance was

determined using p < .05.

For study one, a covariate analysis provided a measure of the strength of the relationships

between all seven domains of classroom climate with math achievement, controlling for

academic aptitude. The three domains that correlated highest with achievement were selected as

measures of classroom climate and designated as involvement, cooperation, and task orientation.

Study two utilized data on motivation and learning styles. Motivation was assessed by

mastery performance, performance avoidance, and performance approach. Learning styles were

reflected in data on extroversion-introversion, practical-imaginative, thinking-feeling, and

organized-flexible. Math achievement was assessed by differences between standardized pre-

and post-test scores. Academic aptitude was assessed by the Matrix Analogies Test -Short

Form. The hypotheses were tested initially for possible gender and grade level differences.

Some gender differences were found. Thus, gender was included as a controlled variable.

Three multiple-regression models were used to examine the impact of classroom climate

on achievement, motivation on achievement, and learning styles on achievement. The final

analyses examined the linear relationship between the predictive variables of classroom climate,










motivation, learning style preferences, grade level, gender, and aptitude on achievement. Non-

significant interactions were dropped, one at a time, from the regression equation. The

proportion of explained variation in math achievement by the predictive variables in confluence

was examined.










CHAPTER 3
RESULTS

Preliminary Analysis of Data

Gender Effects

An independent t-test was conducted to test for gender differences in ratings of classroom

climate, motivation, learning styles preferences, aptitude, and achievement (Table 3.1). Gender

differences were found on student cohesiveness, (p < .03) and teacher support (p < .03).

Compared to males, females reported higher ratings for student cohesiveness and teacher

support. Gender differences were also found on mastery motivation (p < .03). Compared to

males, females reported higher ratings for mastery performance. Gender differences also were

found on extroversion (p < .03). Compared to females, males were more likely to express a

preference for extroversion. Gender differences were found on academic aptitude. Compared to

females, males displayed higher academic aptitude.

Grade Level Effects

An independent t-test was conducted to test for grade level differences in ratings of

classroom climate, motivation, learning styles preferences, aptitude, and achievement (Table 3-

2). No significant grade level differences were found on these variables.

Table 3-1. Gender differences
Gender M SD M Difference t p-value
What Is Happening In my Class (composite score)
Student Cohesiveness male 31.81 5.98 -2.49 -2.21 *.03
female 34.29 4.50
Teacher Support male 27.19 7.17 -3.20 -2.19 *.03
female 30.39 6.78
Cooperation male 26.94 7.88 -3.01 -1.85 .07
female 29.95 7.64
Equity male 33.71 6.52 .00 .00 .10
female 33.71 9.22
Involvement male 24.09 7.74 -2.37 -1.55 .13
female 26.46 6.74
Task Orientation male 33.00 6.39 -1.59 -1.22 .23
female 34.58 6.01










Table 3-1. Continued
What Is Happening In my Class (composite score)
Gender M SD M Difference t p-value
Investigation male 24.02 7.67 .44 .26 .80
female 23.59 8.63
Student Styles Questionnaire (T score)
Extroversion male 22.48 59.71 -26.68 -2.17 *.03
female 49.16 41.41
Thinking male 14.85 56.15 23.18 1.76 .08
female 8.34 57.84
Practical male 8.70 59.78 19.55 1.42 .16
female -10.84 58.81
Organized male -29.57 53.56 -9.72 -.76 .45
female -19.84 57.69
Patterns of Adaptive Learning Survey (composite score)
Mastery male 18.18 5.64 -2.50 -2.18 *.03
female 20.68 4.85
Approach male 11.88 5.70 -.67 -.55 .59
female 12.55 5.71
Avoidance male 10.43 4.15 .32 .36 .72
female 10.11 4.03
Matrix Analogies Test Short Form (percentile ranking)
male 65.27 29.92 13.83 2.41 *.02
female 51.44 25.85
Achievement (z-score)
male .126 .922 .268 1.33 .19
female -.142 1.09



Table 3-2. Means and standard deviations by grade
Grade M SD M Difference t p-value

What Is Happening In my Class (composite score)
Task Orientation 9 32.26 9.11 2.40 1.51 .13
10 29.86 9.54
Investigation 9 24.34 8.01 2.75 1.37 .17
10 21.59 8.26
Equity 9 33.18 9.71 1.81 .911 .36
10 32.65 6.38
Student cohesiveness 9 33.08 5.39 .53 .803 .43
10 32.50 5.34
Involvement 9 25.23 7.45 .41 .652 .52
10 24.82 6.88
Teacher Support 9 28.86 6.70 .81 .304 .76
10 28.05 8.07
Cooperation 9 27.58 9.28 -1.07 .133 .89
10 28.65 7.03
Student Styles Questionnaire (T score)
Thinking-feeling 9 10.97 56.79 21.34 1.62 .11










Table 3-2. Continued.
Gender M SD M Difference t p-value
Student Styles Questionnaire (T score)
10 -10.37 58.28
Practical-imaginative 9 3.76 60.02 -14.08 -1.11 .27
10 10.32 59.24
Extroversion- 9 30.86 56.41 -13.14 -.822 .41
mntroversion
10 44.00 45.17
Organized-Flexible 9 -22.90 55.61 -5.94 .733 .47
10 -28.84 57.09
Patterns of Adaptive Learning Survey (composite score)
Mastery 9 19.70 5.11 2.03 1.42 .16
10 17.67 6.41
Approach 9 11.93 5.93 -1.18 -.792 .43
10 13.11 4.66
Avoidance 9 10.32 3.98 .16 .164 .88
10 10.16 4.50
Matrix Analogies Test Short Form (percentile ranking)
9 60.56 30.44 4.37 .721 .48
10 56.19 23.12
Achievement (percentage)
9 .004 .99 .011 .482 .28
10 -.007 1.06



Race/Ethnicity and Family Income Effects

A one-way ANOVA was conducted to compare means between groups who identified

themselves as Caucasian, African American, Hispanic, Asian/Pacific Islander, and South

American, in ratings of classroom climate, motivation, learning styles preferences, aptitude, and

achievement (Table 3-3). No significant differences were found on these variables for

race/ethnicity.

A one-way ANOVA was conducted to compare means between groups in ratings of

classroom climate, motivation, learning styles preferences, aptitude, and achievement who

identified themselves as having a household income from below twenty thousand annually to

above sixty five thousand annually (Table 3-3). No significant differences were found on these

variables for annual household income.










Table 3-3. Tests for effects between groups by race/ethnicity and income
Measure Race/Ethnicity Annual Household Income
F p-value F p-value
Cooperation 1.772 .11 .399 .75
Student Cohesiveness 1.704 .13 .331 .80
Task Orientation .414 .87 1.111 .35
Avoidance 1.072 .39 2.352 .08
Approach .262 .96 .204 .89
Mastery .538 .78 .221 .88
Extroversion 1.575 .178 1.165 .33
Practical .901 .50 .621 .60
Thinking .810 .57 .995 .40
Organized .319 .93 2.394 .08
Achievement 1.153 .34 .584 .638
Aptitude .805 .57 2.144 .10

A one-way ANOVA was conducted to compare means between groups in ratings of

classroom climate, motivation, learning styles preferences, aptitude, and achievement for those

students who reported mother' s and father' s education level from less than higher school to

advanced degrees. (Table 3-4). No significant differences were found on these variables for

parents' education level.


Table 3-4. Tests for effects between groups by parent education
Measure Mother's Education Father's Education
F p-value F p-value
Cooperation .488 .69 .483 .69
Student Cohesiveness 1.704 .130 .380 .77
Task Orientation .373 .77 .848 .47
Avoidance .429 .73 1.904 .14
Approach 1.530 .21 .094 .96
Mastery .134 .94 .579 .63
Extroversion .931 .44 .119 .95
Practical .853 .47 .495 .69
Thinking 1.720 .17 .405 .75
Organized .80 .50 .959 .42
Achievement 1.437 .23 .584 .63
Involvement 1.575 .17 1.678 .43
Aptitude 2.377 .13 .511 .68












Teacher and Class Period Effects

A one-way ANOVA was conducted to compare means between groups based upon class

period and teacher in ratings of classroom climate, motivation, learning styles preferences,

aptitude, and achievement (Table 3-5). No significant differences were found on these variables.

Table 3-5. Tests for effects between groups by teacher and class period
Measure Teacher Class Period
F p-value F p-value
Cooperation 1.730 .19 1.628 .17
Student Cohesiveness 3.056 .08 .380 .24
Task Orientation .007 .93 .333 .86
Avoidance 7.767 .08 .240 .63
Approach 1.730 .19 2.253 .08
Mastery .2861 .09 2.12 .09
Extroversion .240 .63 .528 .72
Practical .063 .80 .486 .75
Thinking 1.471 .23 .437 .78
Organized .572 .45 .601 .66
Achievement 1.678 .43 .848 .47
Involvement .604 .22 .240 .63
Aptitude 1.468 .22 .134 .94


Study One

Students' Ratings of Classroom Climate Will Predict Math Achievement

The purpose of study one was to examine the effects of classroom climate on achievement.

Using Pearson's correlation, the three subscales for classroom climate were Involvement,

Cooperation, and Task Orientation (see Appendix B-1). Using multiple regression analysis, the

contributions of student ratings of involvement, cooperation, and task orientation on math

achievement were determined after controlling for aptitude, gender, grade level, and

race/ethnicity (Table 3-6). This model accounted for 7.9% of the shared variance: F (103, 7)< 1.05,

p < .40. Four non-significant control variables, gender, grade level, race/ethnicity, and aptitude

were dropped from the subsequent analysis. The final classroom climate model (Table 3-7)










accounted for 4.6% of the shared variance in math achievement: F (103, 7) < 1.40, p < .24.

Involvement contributed significantly to the prediction of achievement: t < -2.01, p < .04.

Table 3-6. Classroom climate full model for prediction of achievement
Subscale Beta t p-value
Involvement -.88 -1.93 .05
Cooperation .73 1.43 .15
Grade .01 .03 .97
Task Orientation .78 1.32 .18
Gender -.37 -1.17 .24
Aptitude -.27 -1.14 .25
Race/ethnicity -.11 -.80 .42
Model Summary R R Square F p-value
.281 079 1.05 .40

Table 3-7. Classroom climate final model for the prediction of achievement
Subscale Beta t p-value
Involvement -.89 -2.01 .04
Cooperation .62 1.24 .21
Task Orientation .23 .48 .62
Model Summary R R Square F p-value
.281 079 1.40 .24



Students' Ratings of Motivation Will Predict Math Achievement

Using multiple regression analysis, the contributions of student ratings of mastery

performance, performance approach, and performance avoidance on math achievement were

determined after controlling for aptitude, gender, grade level, and race/ethnicity (Table 3-8).

This model accounted for 4.3% of the shared variance in math achievement: F (103, 6) < .730, p <

.63. Two non-significant control variables, gender and grade level, were dropped from the

subsequent analysis. The final motivation model accounted for 1.2% of the shared variance in

math achievement: F (103, 6) < .307, p < .87 (Table 3-9).

Table 3-8. Motivation full model for the prediction of achievement
Beta t p-value
Mastery .615 1.425 .16
Gender -.336 -1.114 .26
Grade -.265 -.956 .34
Approach -.228 -.812 .42
Avoidance .173 .653 .51










Table 3-8. Continued
Beta t p-value
Aptitude .025 .108 .91
Model Summary R Rsquare F p-value
.208 .043 .730 .63

Table 3-9. Motivation final model for the prediction of achievement
Beta t p-value
Approach -.290 -1.048 .29
Mastery .261 .714 .47
Aptitude .025 .110 .91
Avoidance .017 .067 .94
Model Summary R R Square F p-value
.111 .012 .307 .87


Study Two

Students' Preferred Learning Styles Will Predict Math Achievement

The purpose of study two was to examine the effects of learning style preferences on math

achievement. Using multiple regression analysis, the contributions of students' preferred

learning styles preferences on math achievement were determined after controlling for aptitude,

gender, grade level, and race/ethnicity (Table 3-10). This model accounted for 10.6% of the

shared variance in math achievement: F (103, 8) < .965, p < .47. Four non-significant control

variables, gender, grade level, race/ethnicity, and aptitude were dropped from the subsequent

analysis. The final motivation model accounted for 6. 1% of the shared variance in math

achievement: F (103, 8)< 1.507, p < .21 (Table 3.11). Thinking-feeling learning style preference

contributed significantly to the prediction of math achievement: t < 2.248, p < .02.

Table 3-10. Preferred learning styles full model for the prediction of achievement
Beta t p-value
Thinking-Feeling .246 2.083 .04
Extroversion-Introversion 198 1.311 .20
Aptitude .218 .930 .36
Race/Ethnicity -.033 -.218 .83
Grade -.204 -.620 .66
Gender -.133 -.444 .90
Practical-Imaginative .053 .409 .68
Organized-Flexible -.111 -.811 .42
Model Sununary R R Square F p-value
.326 .106 .965 .47










Table 3-1 1. Preferred learning styles final model for the prediction of achievement
Beta t p-value
Thinking-Feeling .260 2.248 .02
Extroversion-Introve rsion .087 .740 .46
Organized-Flexible -.053 -.454 .65
Practical-Imaginative -.001 -.005 .99
Model Summary R R Square F p-value
.283 .080 1.507 .21

Contribution of a Confluence of Variables to Math Achievement beyond that Obtained
Individually

Using stepwise multiple regression analysis, the unique contribution of classroom climate,

motivation, and preferred learning styles on math achievement was determined after controlling

for aptitude, gender, grade level, and race/ethnicity (Table 3-12). The full model accounted for

40. 1% of the shared variance in math achievement: F (103, 16) < 1.535, p < .13 (Table 3-13).

Involvement correlated negatively with math achievement: t < -2.79, p < .00. Thinking-feeling

correlated positively with math achievement: t < 3.13, p < .00. Non-significant terms were

dropped, one at a time from the subsequent analysis. The final model (Table 3-14) accounted for

19. 1 % of the shared variance in math achievement: F (103, 3) < 5. 193, p < .00. Two variable

correlated positively with math achievement, thinking-feeling (t <2.809, p < .01) and cooperation

(t < 3.17, p < .00), and one correlated negatively, involvement (t < -3.23, p < .00).

Table 3-12. Unique contribution of variables to the predication of achievement
Variable Contribution
Cooperation .041
Thinking-Feeling ..035
Involvement .015
Task Orientation .015
Gender .007
Extroversion-Introve rsion .006
Race/ethnicity .006
Aptitude .002
Approach .002
Mastery .001
Organized-Flexible .000
Avoidance .000
Grade .000
Teacher Support -.405



















Table 3-13. Full model of confluence of variables for the prediction of achievement
Beta p-value
Cooperation 1.975 1.93 .06
Involvement -3.517 -2.79 .00
Task Orientation .214 1.48 .15
Thinking-Feeling 2.928 3.13 .00
Extroversion-Introve rsion .294 1.45 .15
Student Cohesiveness -1.260 -1.39 .17
Race/ethnicity -.20 9 -1 .1 37 .26
Teacher Support -.405 -.562 .58
Organized-Flexible .081 .466 .64
Mastery .374 .453 .65
Avoidance 162 .410 .68
Practical-Imaginative -.059 -.382 .70
Gender -.161 -.356 .72
Aptitude .082 .273 .787
Grade -.088 -.231 .818
Approach -.063 -.167 .868
Investigation -.105 -.164 .871
Model Summary R R Square F p-value
.633 .401 1.535 .13

Table 3-14. Final model of confluence of variables for the prediction of achievement
Beta t p-value
Involvement -1.683 -3.231 .00
Cooperation 1.648 3.166 .00
Thinking .314 2.809 .01
Model Summary R R Square F p-value
.437 .191 5.193 .00


Table 3-12. Continued
Variable
Practical-Imaginative
Gender
Student Cohesiveness
Investigation


Contribution
.000
-.161
.000
.000









CHAPTER 4
DISCUSSION

The purpose of this study was to examine the effects of classroom climate, motivation, and

students' preferred learning styles on achievement. Students were asked to complete an aptitude

test, an achievement pre-and post-test, a demographic survey, and measures of classroom

climate, preferred learning styles, and motivation. Data was collected from 103 out of 150 ninth

and tenth grade students enrolled in an Algebra I class. Test for effects by teacher, class period,

grade level, household income level, race/ethnicity, and parent education level indicated no

significant differences. Test for gender effects indicated that compared to boys, girls

demonstrated higher aptitude, rated their classroom more favorably for student cohesiveness and

teacher support, and were more likely to report an extroverted preference and a mastery

performance orientation towards math. However, no significant gender differences were found

for these qualifiers when predicting math achievement

Hypotheses

Students' Ratings of Classroom Climate Will Predict Math Achievement

The first goal of this study was to examine the effects of classroom climate on math

achievement. Three of the seven domains of the WIHIC with the strongest correlations to math

achievement, involvement, cooperation, and task orientation, were selected as measures of

classroom climate when predicting math achievement. A significant negative relationship was

found for involvement with math achievement while a significant positive relationship was found

for cooperation. Task orientation did not contribute to math achievement. Thus, students

perceive themselves as less likely to share their ideas or participate with the class, yet who

worked together with peers on assignment completion, demonstrated higher achievement than

those students reporting more class involvement and less peer interaction.










The negative relationship between math achievement and involvement may be interpreted

in developmental terms. Wigfield and colleagues (1998) noted a trend of increased efficacy

coinciding with declining interest and value for math achievement. Thus, students may

demonstrate lower ratings of classroom climate while demonstrating higher scores on math

achievement. Because developmental trends were not addressed, this trend may confound the

variables measured in this study.

This finding also may reflect the instructional style of math or expectations based upon

prior experiences. Instructional styles in math classes typically rely on lectures followed by

independent practice. Students who prefer to work independently may demonstrate a better fit

with and benefit from this instructional style (Wetzel, Potter, & O'Toole, 1982).

Given this finding, students with a preference for introversion would be expected to

perform better than those students with a preference for extroversion. Also, given this finding,

students having a higher task orientation would be expected to be more successful in math.

However, in this study, such results were not found. Extroversion- introversion and task

orientation did not impact math achievement.

One explanation may be due to the differences in measures used to assess achievement,

motivation and learning style preferences. The measure used to assess achievement may not

have been sufficiently sensitive to short term gains or may not have reflected the classroom

curricula (AFT, 2001). Thus, the predictability of achievement by the variables used in this

study may be underestimated. Additionally, the classroom climate scale for involvement does

not measure the same qualities as those measured by the extroversion-introversion scale for

learning styles preference. Involvement measures the extent to which students perceive their

own and others' contribution to class discussion. Introversion assesses the extent to which










students prefer to work independently. Thus, the negative relationship of involvement with math

achievement may be unrelated to students' preferences. The findings may indicate students with

greater adaptability for work, independent of the class instruction, rather than those with an

introversion preference, perform better than those students who require more involvement.

The finding that cooperation impacts math achievement appears to be contrary to the

negative correlation of involvement with achievement. However, this is due to the differences in

qualities measured by the respective domains. Involvement measures the extent to which

students participate with the class discussion and lecture while Cooperation measures the extent

to which students help each other or work together. Thus, if the classroom allows for

opportunities for students to give assistance to each other, this may increase positive ratings for

this domain. Based upon these findings, it is suggested that students who are less inclined to

participate with the class, yet who prefer to work together with peers to complete math

assignments, demonstrate higher achievement gains than students who prefer to work alone or

who more frequently are involved in class discussions and lectures.

The nonsignificant impact of task orientation on math achievement may be related to other

factors extraneous to the task or classroom goals. For example, students striving for future

enrollment in a university may be responding to other goals beyond that which were measured

by task orientation. Additionally, developmental trends indicate decreased interest and increased

efficacy for math as students' progress through secondary school. Thus, students' extraneous

goals as well as developmental trends may have underestimated the impact of task orientation on

math achievement.









Students' Ratings of Motivation will Predict Math Achievement

The second goal of this study was to examine the effects of students' motivation when

predicting math achievement, specifically their level of mastery performance, performance

approach, or performance avoidance.

These three motivational variables had no significant impact on math achievement.

Findings from prior studies indicated higher levels of motivation are associated with

achievement (Davis, 2004; Middleton & Spanias, 1999; Uguroglu, & Walberg, 1979). The

current Eindings may reflect the complexities of motivation related to other factors unrelated to

achievement specific to math. For example, the methods used in this study did not assess

external motivating factors such as achievement related to future college enrollment. College

bound students often are motivated to achieve in most subj ects in order to sustain a high grade

point average required for competitive enrollment. Thus, the students may not be motivated to

perform well in math, yet may be motivated to earn high scores to improve or maintain grade

point averages (Higgins, Strauman, & Klein, 1986; Dweck, 1992; Harackiewicz & Sansone,

1991). Students who are not college bound may have other external motivational factors.

Pressure from parents and peers may serve as motivation to avoid academic failure, rather than

as motivation specific to math achievement. Therefore, while students may not be motivated for

the specific class, they may be motivated for a reason unrelated to the specific classroom

(Emmons, 1992; Little, Lecci, & Watkinson, 1992).

Students' Preferred Learning Styles will Predict Math Achievement

The third goal of this study was to examine the effects of students' preferred learning style

on math achievement. A significant positive relationship was found between thinking-feeling

learning style preference and math achievement. These findings suggest students reporting a









stronger thinking learning styles preference demonstrated larger increases in achievement than

those students reporting a stronger feeling learning styles preference.

Unlike academic domains that require creative or cooperative input, algebra I requires

students to recognize, employ, and execute correct formulas for equations. Tasks requiring

creativity and cooperation are more consistent with styles endorsed by those with a feeling

preference. Tasks that require obj ective execution of formulas are more consistent with styles

endorsed by students with a thinking preference (Cano-Garcia & Hughes, 2000). While

temperament is considered an innate and stable trait (Bates, 1989; Benson, 2005; Buss & Plomin,

1984; Goldsmith et.al., 1987; Joyce, 2000; Kristal, 2005; McCrae et. al., 2000), learning styles

are complex interactions between the individual with his or her environment.

During adolescence, attempts to delineate preferences with expectations of the classroom

environment in a way that measures the effect on achievement may be difficult. Learning styles

emerge from a confluence of cognitive, affective, and biological factors (Keefe, 1991; Thomas,

Chess, & Birch, 1968). Additionally, individual modes of perception, processing, memory, and

cognition create a cognitive style for the individual based upon prior experiences (Keefe, 1991).

Thus, according to Keefe, although preferences are inborn, they are subject to the influences of

development and experience.

Confounding the role of experience, gender differences in learning styles also are noted

(Eiszler, 2000), with girls reporting more reliance on teacher instruction than boys. Thus,

preference differences in modality and learning styles may have compounded the results for

learning styles preferences. For example, given the negative relationship of involvement with

achievement, introversion could be expected to have a significant impact on math achievement.

Yet, extroversion-introversion was found to be nonsignificant. Confounding these results,










gender differences in both learning styles preferences and math achievement were noted.

Compared to females, males reported stronger extroversion preferences and demonstrated higher

math achievement. Thus, while classroom instructional style appears to be more conducive to

those students with introversion preferences, the results may have been confounded by the

gender differences in modality preference. Because of the complex interaction of experience and

temperament, future studies are needed to better understand the effects of learning style

preferences as they relate to classroom achievement, both directly and indirectly.

Contribution of a Confluence of Variables to Math Achievement Beyond that Obtained
Individually

The fourth goal of this study tested the hypothesis that the confluence of classroom

climate, motivation, and preferred learning styles would contribute to math achievement beyond

that obtained when these variables were used individually. As expected, the significant

contributing qualities predicted more variance in math achievement, in confluence, than when

each quality is considered alone. Students reporting a stronger preference for thinking learning

styles preference, more negative perceptions of involvement, and more positive perceptions of

cooperation demonstrated higher math achievement than those students reporting a stronger

feeling learning styles preference, more positive perceptions of involvement, and less positive

perceptions of cooperation. Motivation did not predict math achievement.

Although Davis, Davis, and Smith (2004) found classroom climate and motivation predict

math achievement, their studies employed different measures with different theoretical

underpinnings. For example, measures of rule clarity and order/organization of the Classroom

Environment Scale (Moos & Trickett, 1973) were used to measure classroom climate when

predicting math achievement in middle school (Davis, 2004; Davis, Davis, & Smith, 2004). ).

These qualities are not measured by the What Is Happening In this Class scale (Fraser, Fisher, &










McRobbie, 1996). Eccles' expectancy value model of motivation also predicted math

achievement in middle school students (Davis, 2004; Davis, Davis, & Smith, 2004). The

qualities included in this model are not measured by the Patterns of Adaptive Learning Survey

(Midgley, et al., 1996). Thus, these results suggest a measure's theoretical considerations are

important when assessing classroom climate and motivation as well as predicting math

achievement.

The results of this study raise several important questions. What qualities of classroom

climate are important when predicting math achievement? In this study, no relationship was

found for many factors of classroom climate with achievement. However, in prior studies,

aspects of classroom climate are significant in predicting achievement. Thus, an examination of

individually identified qualities of classroom climate impact the learner, whether it be

academically, behaviorally, and/or socioemotionally may be beneficial. A better understanding

of classroom climate would enable teachers to construct their classrooms in a manner that

promotes classroom and instructional goals.

Another important question is how does motivation affect achievement? Given the

nonsignifieant role of motivation in predicting math achievement in this study, these Eindings do

not suggest the lack of importance of motivation on achievement but rather the measures did not

capture relevant motivational qualities important to students in algebra I classes. A comparative

study of motivational measures on math achievement may shed some light on the adolescents'

motivation for math.

An understanding of the complex interactions of the learner and the learning environment

specific to math may have important implications. Trends that demonstrate reduced motivation









for math achievement among adolescents and inferior performance by females may be better

understood through the developmental perspective of temperament.

Implications

An understanding of the contributions of individual and environmental characteristics on

the promotion of academic achievement is necessary to optimize the educational process.

Academic achievement affects performance on mandatory statewide test scores, college

placement exams, and grade point averages, and can have a functional impact on one's career

and other life events. One's level of achievement in high school may be a critical factor in

acceptance into quality post-secondary educational settings and in competitive employment.

However, other aspects of the classroom environment are important beyond academic

achievement, such as the development of positive self-concept and socio-emotional well-being.

Orientation toward competence (Harackiewicz & Elliott, 1993), self-efficacy (Bandura,

1991), recognition of multiple goals (Barron & Harackiewicz, 2001), and the pursuit of various

end states are important for understanding motivation for academic achievement (Boekaerts, de

koning, and Vedder, 2006). Thus, students may address conflicting goals that balance the need

for socioemotional development with academic development. Positive classroom climate has

been associated with increased involvement(Al spaugh, 1998), feelings of belonging (Goodenow,

1995; Osterman, 2000), social satisfaction (Townsend & Hicks, 1997), improved quality of life

within the classroom (DeYoung, 1977; Gottfredson and Gottfredson, 1989; Hayes, Ryan, &

Zeller, 1994;Mayer & Mitchell, 1993), increased self-efficacy (Bandura, 1991; House, 2002;

Jackson, 2002), and positive self-concept (Crohn, 1983). Classroom climate has also been

associated with higher attendance rates, lower drop out rates (Battistich & Horn, 1997; Resnick

et al., 1997), and higher academic performance (Davis, 2004; Davis, Davis, & Smith, 2004; Goh

& Fraser, 1995).









Academic performance in high school math is reliant upon the development of complex

processes that move toward high-level abstraction and cognitive restructuring. For example, in

algebra, students learn new meanings to mathematical symbols, learn to solve equations by

operation rather than by left to right sequencing, and learn that a solution is not necessarily fixed

nor is it numeric (Thomas & Tall, 2001). Thus, students must acquire the ability to process

algebraic information while contemplating the myriad of possibilities or hypothetical values for

the unknown x (Clement, 1982; Thomas & Tall, 2001). The acquisition and development of

such ability is reliant upon the development of abstract and hypothetical thinking (Piaget, 1985).

In geometry, students must rely on visual spatial processes to understand complex

relationships among geometric shapes. Thus math readiness and math achievement may be more

reliant on exposure to learning and opportunities for practice (Gamoran, 1987) than aptitude. For

example, early exposure to mathematics may affect opportunities for learning as early as middle

school. Exposure to math curriculum impacts level of achievement. Opportunities for learning

differs through the stratification and quality of school coursework (Gamoran), as well as gender

and race/ethnicity (Cambis, 1994), and socioeconomic status (Gamoran, 1987; Lee & Smith,

1997). Students who are low achieving are generally tracked into math classes that provide

limited opportunity for learning new math skills Additionally, low achieving classes tend to be

over representative of students in with low socio-economic status.

Other factors related to opportunities for the development of higher order math skills for

higher math achievement were school social composition related to racial/ethnic diversity (Lee &

Bryk, 1989; Lee & Smith, 1997), variability in SES (Gamoran, 1987; Lee & Bryk, 1989; Lee &

Smith), as well as school's academic emphasis, school climate (Chen & Stevenson, 1995), and

school size (Lee & Smith, 1997). Smaller schools with less racial/ethnic diversity and









overrepresentation of low SES (Lee & Bryk, 1989; Lee & Smith) were associated with less

variability in mathematic courses and lower math achievement (Lee & Bryk, 1989; Lee &

Smith, 1997). Individual factors advocating higher math achievement were related to positive

attitudes towards math achievement, having the belief that effort, not aptitude is important, and

high expectations from parents and peers (Chen & Stevenson, 1995).

Thus, academic aptitude and prior math attainment may not be consistent predictors of

high school math achievement (Clement, 1982; Thomas & Tall, 2001). As a result, many

students may experience a decreased level of motivation resulting from the difficulty they

encounter, leading to reduced feelings of academic self-efficacy (Paj ares, & Graham, 1999;

Wigfield, 1994).

Low academic self-efficacy may result in fear of failure, which may lead students to

avoidance achievement goals (Elliott, 1997; Pajares, & Graham, 1999). According to Elliott, the

adoption of performance avoidance goals can have long-term negative effects on end of the

semester self-reports of well-being. Indeed, Boekaerts, de koning, and Vedder (2006) posit the

avoidance goal oriented individual must reference all possible failure combinations in order to

address avoidance at each level.

Performance avoidance goals may contribute to performance anxiety and detract from

completing immediate tasks successfully (Epstein, & Harackiewicz, 1992; Green, 1980; Trope,

1975). By contrast, students oriented toward performance approach tend to seek out tasks that

are challenging, provide the opportunity to demonstrate competence, assess their ability, and

provide feedback (Epstein, & Harackiewicz, 1992; Kuhl, 1978; Trope, 1975).

Approach and avoidance goal orientations may be inherent traits, such as temperament

(Elliott & Thrash, 2002). According to Elliott and Thrash, performance approach orientation










was predicted by temperament characteristics, such as extroversion and positive emotionality,

while negative emotionality and neuroticism were linked to performance avoidance orientation.

The importance of learning styles preference in achievement is evident. Previous studies

have found learning styles affect achievement (Bajraktarevic, Hall, and Fullick, 2003; Cano-

Garcia & Hughes, 2000; Charkin, O'Toole, & Wetzel, 1985; Ennis-Cole, 2006). Although

instructional styles are often adopted for their ease of execution in mainstream classes with

varying abilities, the provision of greater congruence between the instructional style and learning

style may enhance academic achievement and promote positive attitudes toward learning

(Charkin, O'Toole, & Wetzel, 1985).

Student achievement is higher when the instructional styles and materials are matched to

the students' preferred learning styles (Bajraktarevic, Hall, & Fullick, 2003). Students studying

economics who prefer to work independently, to follow established rules and procedures, and to

execute pre-existing formula demonstrated higher achievement than students who prefer

creative, collaborative, and flexible styles (Cano-Garcia & Hughes, 2000). In this study, students

with a thinking preference displayed higher math achievement than those reporting a feeling

preference. Qualities reported by thinking preferences appear to be more consistent with the

style of classroom instruction (Cano-Garcia & Hughes, 2000), the type of tasks required

(Bajraktarevic, Hall, and Fullick, 2003), and the organizational structure of math classes (Ennis-

Cole, 2006) in which competitive and individual effort is required while using preestablished

formulas and facts. Understanding the role of temperament and preferred learning styles in

academic outcomes may generate classroom interventions designed to enhance the learning

experience of all students.









An improved understanding of student learning style preferences and teacher instructional

style may uncover ways to better construct the classroom instructional environment to address

the diversity of individual learning needs. Using temperament qualities to increase congruence

between learning styles and teaching styles may promote content mastery, enhance the

acquisition of critical thinking skills, and aid students in the mastery of more complex content

(Schroeder, 2006). Varied instructional practices provide a multimodal approach to teaching and

help to address the varied needs of the individual students (Ennis-Cole, 2006).

Instructional practices are found to affect achievement. For example, teachers who focus

on the development of student learning with the goal of facilitating learning are more likely to

plan effective lessons (Femnandez, Cannon, & Chokshi, 2003; Stewart & Brendefur, 2005).

Lessons should be planned with an empathetic perspective to student learning (Lewis, Perry, &

Murata, 2003; Stewart & Brendefur, 2005) by with identifying goals for students and gaining an

understanding of how and why students learn (Lewis, Perry, & Hurd, 2004; Stewart &

Brendefur, 2005). Hawley & Valli (1999) suggest lessons plans should focus on closing the gap

between student performances in relation to the expectations of educational outcomes.

According to Stewart & Brendefur, the lesson plan model links teacher learning and

knowledge to student learning through collaboration teams. Teachers meet to share ideas,

experiences, and knowledge (Lewis, Perry, & Hurd, 2004; Stewart & Brendefur, 2005) for the

creation of plans that address content and context to maximize student learning and engagement

while addressing student needs. Thus, the individual characteristics of the teacher and students

are incorporated into a dynamic approach to teaching and learning.









CHAPTER 5
LIMITATIONS AND FUTURE STUDIES

Various limitations may have attenuated the study of the relationship of classroom climate,

motivation, and temperament on math achievement. The nature of the sample population

deserves attention. Although more diverse high schools were solicited for participation, only one

high school agreed to participate in this study. The participating school was ranked as an FCAT

A school, indicating higher levels of academic accomplishment than lower B, C, or D rated

schools. Additionally, the student population may be representative of higher socio-economic

status than may have been found in a more diverse high school. Thus, the findings may not

generalize to other school populations.

The measures selected may constitute another limitation. The measure of motivation may

not have accounted for external motivational factors, such as students' value for math.

Additionally, the FCAT sample test may not have been a sufficient measure for math

achievement.

The FCAT sample test is designed to be administered at the beginning of the school year

and re-administered prior to the FCAT administration in early March. Thus, the measure should

have reflected increases in knowledge and skills acquired in the classroom. However, some

students demonstrated lower performance on the posttest compared to the pretest. This may

indicate students guessed on one or both of the measures. This also may suggest the information

measured on the sample test was not directly related to classroom lessons and/or the test was not

sufficiently sensitive to short term gains. Thus, future studies may decide to augment the range

and sensitivity of instruments to identify and measure the contribution of these potentially salient

factors when predicting math achievement.










Lack of consideration for external influences may constitute another limitation. This study

did not control for external qualities that may have impacted student learning. For example, the

importance of performing well on the FCAT, meeting parents' expectations, or increasing or

maintaining grade point average for college enrollment may have been motivational factors not

considered. Including a measure of individual value for math may be an important factor for

future studies.

Students' expectations for math class performance may have contributed to their ratings of

classroom climate. For example, given the traditional instructional style of math that

incorporates lecture with independent practice, students may rate the classroom involvement

differently if asked to rate a political science class where expectations for classroom discussions

may be higher. Inclusion of data on expectations for classroom climate compared to perceptions

of actual classroom climate may provide a more comprehensive measure to explain variability in

achievement.

Finally, this study did not address developmental trends in achievement, motivation, and

perceptions of classroom climate. For example, the age and grade levels selected for this study

may have presented a confounding variable not anticipated by this study. Future studies may

wish to assess the influence of these qualities on classroom climate perceptions, motivation,

learning style preferences, and achievement.

The results of this study raise many new questions for future studies. A better

understanding of the role of multiple goals, the influence of prior experiences, the developmental

status of the participants, the type of instruction, and the readiness for math may be needed to

better understand their impact on achievement. Additionally, due to the developmental role of

hypothetical thinking on algebra readiness, future studies may wish to examine the impact of










classroom climate, motivation, and learning style preferences on both algebraic and non-

algebraic math achievement and achievement in other academic domains. Finally, although this

study examined the relationship of preferred learning styles as they relate to math achievement.

Future studies may see the value of examining the effects of preferred learning styles on ratings

of classroom climate and motivation.









APPENDIX A
FORMS

A-1 Consent to Participate

Informed Consent to Participate

Protocol Title: The effects of motivation, preferred learning styles, and perceptions of classroom
climate on achievement in 10th grade algebra students

Please read this consent document carefully before considering your child as a participant
in this study. If you wish your child to participate, please complete and return the consent
form with your child

Purpose of the research study:
The purpose of this study is to examine the learning styles preferences, self-reported motivation
for learning math, and perceptions of classroom climate as it affects achievement for 10th grade
algebra students.

What your child will be asked to do in the study:
If your child is allowed to participate, he/she will be asked to complete a pre- and post-test in
algebra, a nonverbal test of intelligence, and surveys assessing their interest in math, level of
motivation to succeed in math, and their opinion of the classroom environment for math. A short
demographic survey will include rate of absences, socio-economic status, gender, age, and
ethnicity. Your child will not have to respond to any questions they do not wish to and may
withdraw from participation at any time, without penalty. The assessment will be administered
during class time. Participation will not affect your child's grade.

Time required:
Time required for group administration is approximately 1.5 hours for each administration, a
total of 3.0 hours.

Risks and Benefits:
While there are no risks for your child, benefits include a better understanding of the how and
why students are inclined to perform better in areas of mathematics.

Confidentiality:
Your child' s identity will be kept confidential to the extent provided by law. If you wish your
child to participate, your child's information will be assigned a code number. The list
connecting your child's name to this number will be kept in a locked file. When the study is
completed and the data have been analyzed, the list will be destroyed. Your child's name will
not be used in any report.
Voluntary participation:
Your child's participation in this study is completely voluntary. There is no penalty for not
participating.
Right to withdraw from the study:
You have the right to withdraw your child from the study at anytime without consequence.











Whom to contact if you have questions about the study:
Susan Davis, Graduate Student, Department of Educational Psychology, University of Florida
392-0723 suedavis@ufl.edu

Thomas Oakland, PhD, Department of Educational Psychology, University of Florida 392-0723
oakland@ufl.edu

Whom to contact about your rights as a research participant in the study:
UFIRB Office, Box 112250, University of Florida, Gainesville, FL 32611-2250; ph 392-0433.

Consent to Participate
I have read the procedure described above. I give permission for my child to participate...
Child's Name
Parent: Date:












A-2 Student Assent Form


Student Assent Form

To student:

My name is Susan Davis and I am a graduate student at the University of Florida' s Department
of Educational Psychology. I am conducting a study that will be used to examine various factors
that help students learn math.

You have been asked to participate in a study that will be used to help identify these factors. If
you agree to participate, you will be asked to complete a math test to determine achievement
levels for algebra during class time. You will be asked to complete questionnaires that will help
identify how you feel about learning math as well as how you perceive the math classroom. You
are not required to answer any questions you do not wish to and you may withdraw from the
study at any time without penalty. Your participation or refusal to participate will not affect your
grade.

I have read the procedure described above. I agree to participate in the procedure and I have
received a copy of this description.

Name (please print)

Signature Date

Are you willing to participate in the research proj ect? Yes No













Table 6-1. Descriptive statistics for full model of variables
M SD Partial
Corrrelations
IQ (percentile) 59.65 29.02 .12
Achievement (z- 1.14 1.05 1.0
score)
Temperament (T
score)
Extroversion 33.71 54.12 .10
Thinking 5.08 57.64 .26
Practical .47 59.77 -.02
Organized -25.47 55.17 -.07
Class Climate
(Composites)
Task Orientation 33.70 6.25 -.14
Equity 33.70 7.78 .02
Student 32.90 5.49 -.05
Cohesiveness
Teacher Support 28.60 7.14 -.12
Cooperation 28.27 7.88 -.20
Involvement 25.14 7.37 -.21
Investigation 23.83 8.06 .04
Motivation
(Composites)
Mastery 19.28 5.43 .00
Approach 12.17 5.68 -.08
Avoidance 10.29 4.07 .02


Table 6-2. Means and standard deviations for final model of confluence of variables


APPENDIX B
MISCELLANEOUS TABLES


Mean Composite Score
25.14
28.27
Mean T-score
5.079


SD
7.37
7.87

57.64


Involvement
Cooperation

Thinking










LIST OF REFERENCES


Acredolo, L. (1978). Development of spatial orientation in infants. Developmental Psychology,
13, 1-8.

Aldridge, J. & Fraser, B. (1997). Examining science classroom environments in a cross-national
study. Proceedings Westemn Australian Institute for Educational Research Forum.

Aldridge, J. & Fraser, B. (2000). A cross-cultural study of classroom learning environments in
Australia and Taiwan. Learning Environment Research, 3, 101-104.

Allen, D. & Fraser, B. (2002). Parent and student perceptions of the classroom learning
environment and its influence on student outcomes. Paper presented to the American
Educational Research Association. New Orleans, LA.

Alspaugh, J. (1998). The relationship of school and community characteristics to high school
dropout rates. The Clearinghouse, 71, 184-188.

Altorf, M. (2005). Temperament and Metaphysics: A Study on James's Pragmatism and Plato's
Sophistes. Paper presented at the University of Nijmegen, Nijmegen, Netherlands.

American Federation of Teachers (2001). Making standards matter. American Educator, 25,
47-48.

Ames, C. (1992). Achievement goals and classroom motivational climate. In D. Schunk & J.
Meece (Eds.) Students perception of the classroom (pp327-348). Hillsdale, NJ: Lawrence
Erlbaum.

Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students' learning
strategies and motivation processes. Journal of~ducationalPsychology, 80, 260-267.

Anderman, E., Urdan, T., & Roeser, F. (2003). The Patterns of adaptive learning survey, history,
development, and psychometric properties. Paper prepared for the Indicators of Positive
Development Conference.

Anderman, L. (1999). Classroom goal orientation, school belonging and social goals as
predictors of students' positive and negative affect following the transition to middle
school. Journal ofResearch and Development in Education, 32, 89-103.

Anderman, L. & Anderman, E. (1999). Social predictors of change in students achievement goal
orientations. Contemporary Educational Psychology, 24, 21-37.

Anderson, E. (2002). Individual levels of school belongingness predicted higher GPA, greater
general optimism, negatively predicted depression, social regulation, and behavior
problems in school. In M~otivating students, improving schools, advances motivation and
achievement. Hillsdale, NJ: Erlbaum.










Anderson, L., Stevens, D., Prawat, R., & Nickerson, J. (1988). Classroom task environments and
students' task-related beliefs. Elementary School Joumnal, 88, 281-295.

Ardilo, A., & Mareno, S., (2001). Directions in research in cross-cultural neuropsychology.
Journal of Clinical and Experimental Neuropsychology, 77, 143-150.

Aronson, E. (1999). The Power of self-persuasion. American Psychologist, 54, 875-885.

Atkinson, J. (1980). Motivational determinants of risk taking behavior. In E. Higgins & A.
Kruglanski (Eds.) Motivational science: social and personality perspectives. Ann Arbor,
MI: Sheridan Brooks-Braun-Brumfield.

Attaway, N. (2004). Understanding academic motivation in middle school students: Association
with school belonging. Dissertation Abstracts International: Section B: the Sciences and
Engineering, 64, 6314.

Bajraktarevic, N., Hall, W., & Fullick, P. (2003). Incorporating learning styles in hypermedia
environment: Empirical evaluation. Paper presented at the Workshop on Adaptive
Hypermedia and Adaptive Web-Based Systems 2003. Nottingham, United Kingdom

Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37,
122-147.

Bandura, A. (1986). Social foundations of thought and action: A Social cognitive theory.
Englewood Cliffs, NJ: Prentice Hall.

Bardwell, R. (1984). The Development and motivational functions of expectations. American
Educational Research Joumnal 21, 461-472.

Bargar, R. & Hoover, R. (1984). Psychological type and matching of cognitive styles. Theory
into Practice, 23, 56-63

Barron, K. & Harackiewicz, J. (2001). Achievement goals and optimal motivation: Testing
multiple goal models. Journal of Personality and Social Psychology. 80, 706-722.

Bassett, K. (2004). Temperament preferences for children ages 8 through 17 in a nationally
represented sample. Dissertation presented at the University of Florida. Gainesville, FL.

Bates, J. (1986). The measurement of temperament. In R. Plomin and J. Dunn (Eds.) The study
of temperament: changes, continuities, and challenges (pp. 1-11). Hillsdale, NJ: Erlbaum.

Bates, J. (1989). Applications of temperament concepts. In G.A. Kohnstamm, J.E. Bates &
M.K. Rothbart (Eds.) Temperament in childhood (pp 321-355). Chichester, England: John
Wiley & Sons, Ltd.

Bates, J. & Wachs, T. (1994). Temperament: individual differences at the interface of biology
and behavior. Washington DC: American Psychological Association.










Battistich, V., & Horn, A. (1997). The relationship between students' sense of their school as a
community and their involvement in problem behaviors. American Journal of Public
Health, 87, 1997-2001.

Beck, R. (1978). Motivation theories and principles. Englewood Cliffs, New Jersey: Prentice-
Hall, Inc.

Beilstein, C. & Wilson, J. (2000). Landmarks in route learning by girls and boys. Perceptual &
motor skills, 91, 877-882.

Benson, N. (2005). Cross-national construct equivalence of school-age children's temperament
types as measured by the student styles questionnaire. Dissertation presented at the
University of Florida. Gainesville, FL.

Berens, L. (2000) Understanding yourself and others: An Introduction to temperament. London:
Durtro Press.

Black, A. (1965). On the Combination of drive and incentive motivation. Psychological
Review, 72,310-317.

Boekaerts, M., de koning, E., & Vedder, P. (2006). Goal directed behavior and contextual
factors in the classroom: an innovative approach to the study of multiple goals.
Educational Psychologist, 41, 33-51.

Boeree, G., (2005). Carl Jung 1875-1961. Personality Theories. E-text presented to
Shippensburg University. Shippensburg, Pa.

Bower, B. (2005). DNA's moody temperament. Science News 167, 308-309.

Brophy, J. (2001). Motivating students to learn. Hillsdale, NJ: Lawrence Erlbaum Associates,
Inc.

Brunstein, J. (1993). Personal goals and subjective well-being: A Longitudinal study. Journal
of Personality and Social Psychology, 65, 1061-1070.

Buss, D. & Plomin, R. (1984). Temperament: Early developing personality traits. Mahwah, NJ:
Lawrence Erlbaum Associates.

Cano-Garcia, F., Hughes, E., (2000). Learning and Thinking Styles: an analysis of their
interrelationship and influence on academic achievement. Educational Psychology, 20,
413-430.

Calkins, S. & Fox, N. (1992). The relations among infant temperament, security of attachment
and behavioral inhibition at twenty-four months. Child Development, 63, 1456-1472.

Cambis, S. (1994). The path to math: gender and racial-ethnic differences in mathematics
participation from middle school to high school. Sociology of Education,67, 199-215.










Casteel,C. (1998). Teacher-student interactions and race in integrated classrooms. Journal of
Educational Research, 92, 115-120.

Chapin, S., & Eastman, K. (1996). External and internal characteristics of learning
environments. Mathematics Teacher, 89, 112-115.

Charkin, R., O'Toole, D., & Wetzel, J. (1985). Linking teacher and student learning styles with
student achievement and attitudes. The Journal of Economic Education, 16, 1 11-1 13.

Chatterji, M. (2004). Good and bad news about Florida student achievement: Performance
trends on multiple indicators since passage of the A+ legislation. Educational Policy Brief.
Doc No.EPSL-0401-1 05-EPRU,Tempe,AZ: Educational Policy Studies Laboratory.

Chatterji, M.(2005). Closing Florida's achievement gap. Jacksonville, FL: Florida Institute of
Education at the University of North Florida.

Chen, C. & Stevenson, H. (1995). Motivation and mathematics,: a comparative study of Asian-
American, Caucasian American, and East Asian high school students. Child Development,
66, 1215-1234.

Chess, S. & Thomas, A. (1996) Temperament theory and practice. New York: Guilford Press

Chess, S. & Thomas, A. (1996). Goodness of fit: Clinical applications from infancy through
adult life. New York: Brunner/Mazel.

Chionh, Y. & Fraser, B. (1998). Validation and use of the "What is Happening In This Class"
Questionnaire in Singapore. Paper presented at Annual Meeting of the American
Educational Research Association, San Diego, CA.

Chipuer, H., Bramston, P. & Petty, G. (2003). Determinants of subjective quality of life among
rural adolescents: a developmental perspective. Social Indicators Research, 61, 79-95.

Clement, J., (1982). Algebra word problem solutions: Thought processes underlying a common
misconception. Journal for Research in Mathematics Education, 13, 16-30.

Cornett, C. (1983). What you should know about teaching and learning styles. Bloomington,
IN:. Phi Delta Kappa Educational Foundation.

Cranton, P. &, Knoop, R. (1995). Assessing Jung's psychological types: The PET Type Check.
Genetic, Social & General Psychology Monographs, 121, 249-275

Creemers, B. (1994). The effective classroom. London: Cassell.

Crohn, L. (1983). Towards excellence: student and teacher behaviors predictors of school
success. Research summary report presented at Northwest Regional Educational
Laboratory. Portland, Or.










Dart, B., Burnett, P., & Purdie, N. (2000). Students' conception of leaming, the classroom
environment, and approaches to learning. The Journal of Educational Research, 93, 262-
270.

Davis, H., Davis, S., & Smith-Bonahue, T. (2004). Exploring the social contexts of motivation
and achievement: the role of relationship quality, classroom climate, and subj ect matter.
Unpublished manuscript.

Davis, S. (2004). The role of students' perceptions of classroom climate in predicting academic
motivation and assigned grades in middle school mathematics. Thesis presented at the
University of Florida. Gainesville, FL.

Deci, E. & Ryan, R. (2002). Handbook of self-determination research. Rochester, NY:
University of Rochester Press.

Deci, E., Vallerand, R., Pelletier, L., & Ryan, R. (1991). Motivation and education. Educational
Psychologist, 26, 325-346.

Denny, D., & Turner, R. (1969). Teacher characteristics, classroom behavior, and growth in
pupil creativity. The Elementary School Journal, 69, 265-270.

Derryberry, D., & Reed, M. (1994). Temperament and attention: Orienting toward and away
from positive and negative signals. Journal of Personality and Social Psychology, 66,
1128-1139.

Dweck, C. (1992). The study of goals in psychology. Psychological Science, 3, 165-167.

Dweck, C. (1999). Self-theories: their role in motivation, personality, and development.
Philadelphia, PA: Psychological Press.

Dweck, C. & Leggett, E. (1988). A socio-cognitive approach to motivation and personality.
Psychological Review, 95,256-273.

Eccles, J. (1983). Expectancies, values, and academic behaviors. In J.T.Spence (Ed.),
Achievement, and Achievement Motives: Psychological and Sociological Approaches (pp.
75-146). San Francisco: W.H.Freeman.

Eccles, S., Wigfield, A., Midgely, C., Reuman, D., MacIver, D., & Feldlaufer, H. (1993).
Negative effects of traditional middle schools on students' motivation. The Elementary
School Joumnal, 93, 555-591.

Eccles, J., Wong, C., & Peck,S. (2006). Ethnicity as a social construct for the development of
African American adolescents. Journal of School Psychology,44, 407-426.

Ellis, S. (1996). Staff development is the key to school reform: an interview with Efrain Vila.
Journal of Staff Development, 17, 52.










Elliott, A. (1997). Avoidance achievement motivation: a personal goals analysis. Journal of
Personality and Social Psychology, 73, 171-185.

Elliot, A., & Church, M. (1997). A hierarchical model of approach and avoidance achievement
motivation. Journal of Personality and Social Psychology, 72, 218-232.

Elliot, A., & Harackiewicz, J. (1996). Approach and avoidance achievement goals and intrinsic
motivation: A Mediational analysis. Journal of Personality and Social Psychology, 70,
461-475.

Elliot, A. & McGregor, S. (2001). A 2x2 achievement goal framework. Journal of Personality,
80, 501-519.

Emmons, R. (1986). Personal strivings: An approach to personality and subjective well-being.
Journal of Personality and Social Psychology, 51, 1058-1068.

Emmons, R. (1992). Abstract versus concrete goals: Personal striving level, physical illness, and
psychological well-being. Journal of Personality and Social Psychology, 62, 292-300

Ennis-Cole, D. (2006). Positive learning places. Presented at the Texas Computer Education
Association Conference, Richardson, TX.

Epstein, J. A., & Harackiewicz,J. M.(1992). Winning is not enough: The Effects of competition
and achievement orientation on intrinsic interest. Personality and Social Psychology
Bulletin, 18, 128-1 39.http://psycinfo.apa.org.1p.hscl.ufl.edu/o/eud f~i=976-
09902-001

Epstein, S. (1994). Integration of the cognitive and psychodynamic unconscious. American
Psychologist, 49, 707-724.

Fernandez, C., Cannon, J. & Chokshi, S. (2003). A US-Japan lesson study collaboration reveals
critical lenses for examining practice. Teaching and Teacher Education 19, 171-185

Ferrar, M. (1994). Validity of the STAR: student styles questionnaire: racial-ethnic
comparisons. Presented at the University of Texas at Austin. Austin, TX.

Ford, M. (1992). Motivating humans: goals, emotions, and personal agency beliefs. Newberry
Park, CA.

Fraser, B. (1986). Classroom environment. Dover, NH.: Croom Helm Ltd.

Fraser, B. (1988). Classroom environment instruments: development, validity, and applications.
Learning Environments Research An International Joumnal, 7, 1-33.

Fraser, B. (2002). Learning environment research: Yesterday, today and tomorrow. In Goh,S. &
Khine, M. (Eds.), Studies in educational learning environments: an international
perspective, 1-26. Singapore: World Scientific.










Fraser, B., Fisher, D. & McRobbie, C. (1996). Development, validation and use of personal and
class forms of a new classroom environment instrument. Paper presented at the annual
meeting of the American Educational Research Association: NY.

Fraser, B. & Walberg, H. (1991). Educational environments: evaluation, antecedents, and
consequences. Elmsford, NY: Pergammon Press, Inc.

Freeman, K. (2004). The significance of motivational culture in schools serving African
American adolescence: A goal theory approach. In Pintrich, P & Machr, M. (Eds.)
Motivating students, improving schools, advances motivation and achievement, the legacy
of Carol Midgely, 13,(pp 65-95). Toronto, Ontario: Elsevier, Ltd.

Gaith, G. (2003). The relationship between forms of instruction, achievement, and classroom
climate. Educational research, 45, 83-93.

Gallagher, A. & Kaufman,J. (2005). Gender differences in mathematics. New York: Cambridge
University Press.

Gamoran, A. (1987). The stratification of high school learning opportunities. Sociology of
Education, 3, 135-155.

Gamoran, A., Porter, A., Smithson, J., & White, P. (1997). Upgrading high school mathematics
instruction: improving learning opportunities for low achieving, low income youth.
Educational Evaluation and Policy Analysis, 19, 325-338.

Gardner, A., Mason, C., & Matyas, M. (1989). Equity, excellence and "just plain good
teaching." The American Biology Teacher, 51, 72-77.

Geary,D., Saults,S., Liu,F. & Hoard,M. (2000). Sex differences in spatial cognition,
computational fluency, and arithmetic reasoning. Journal of Experimental Child
Psychology, 77, 337-353.

Goh, S., & Fraser, B. (1995). Learning environment and student outcomes in primary
mathematics. Paper presented at the Annual Meeting of the American Educational
Research Association. San Francisco, CA.

Gottfredson, G., & Gottfredson, D. (1989). School climate, academic performance, attendance,
and dropout. US Dept of Education.

Goldsmith, H., Buss, A., Plomin, R., Rothbart, M., Thomas, A., Chess, S., Hinde, R., McCall, R.
(1987). Roundtable: What is temperament? Four approaches. Child Development, 58,
505-529.

Goodenow, C. (1993a). The psychological sense of school membership among adolescents:
scale development and educational correlates. Psychology in the schools, 30, 79-90.

Goodenow, C. (1993b). Classroom belonging among early adolescent students. Journal of Early
Adolescence, 13, 21-43.










Goodenow, C. (1995). Conceptualizing and measuring classroom belonging and support among
adolescents. Unpublished paper presented to Tufts University. Boston, MA.

Gouteux, S. & Spelke, E. (2001). Children' s use of geometry and landmarks to reorient open
space. Cognition, 81, 119-148.

Griffin, S. & Case,R. (1996). Evaluating the breadth and depth of training effects when central
conceptual structures are taught. Monographs of the Society for Research in Child
Development, 61, 83-102.

Haertel, G., Walberg, H., & Haertel, E. (1981). Social-psychological environments and learning:
a quantitative synthesis. British Educational Research Journal, 7, 27-36.

Hagen, A. & Weinstein, C. (1995). Achievement goals, self-regulated learning, and the
classroom context. New Directions for Teaching for Learning, 63, 43-55.

Halpern, D. (2000). Sex differences in cognitive abilities (3rd ed.). Mahwah, NJ: Erlbaum.

Harter, S., & Connell, J. P. (1984). A model of children's achievement and related self-
perceptions of competence, control, and motivational orientation. In J. Nicholls & M.
Machr (Eds.), Advances in motivation and achievement, 3. The development of
achievement motivation. London: JAI Press.

Harackiewicz, J., & Manderlink, G. (1984). A process analysis of the effects of performance-
contingent rewards on intrinsic motivation. Journal of Experimental Social Psychology,
20, 531-551.

Harackiewicz, J., & Sansone, C. (1991). Goals and intrinsic motivation: You can get there
from here. Advances in Motivation and Achievement, 7, 21-49.

Hawley, D. & Valli, L. (1999). The essentials of effective professional development: a new
consensus. In L. Darling-Hammond & G. Sykes (Eds.) Teaching as the learning
profession. Handbook of policy and practice (pp 127-150). San Fransicso: JosseyBass
Publishers.

Hayes, S. (1999). Comparison of the Kaufman Brief Intelligence Test and the Matrix Analogies
Test--Short Form in an Adolescent Forensic Population. Psychological Assessment, 11,
108-110.

Heider, F. (1958). The psychology of interpersonal relations. Wiley: New York.

Herner, L. & Spelke, E. (1994). A geometric process for spatial reorientation in young children.
Nature, 370, 57-59.

Hespos, S. & Rocaht, P. (1997). Dynamic representation in infancy. Cognition, 64, 153-189.










Higgins, E. T., Strauman, T., & Klein, R. (1986). Standards and the process of self-evaluation:
Multiple affects from multiple stages. Handbook of motivation and cognition:
Foundations of social behavior, 1, 23-63.

Horton, C. & Oakland, T. (1997). Temperament-based learning styles as moderators of
academic achievement. Adolescence, 32, 131-141.

House, D. (2002). The independent effects of student characteristics and instructional activities
on achievement: an application of the input-environment- outcome assessment model.
International Journal of Instructional Media, 29, 225-239

Hunt, J. (1965). Intrinsic motivation and its role in psychological development. In D. Levine
(Ed.), Nebraska symposium on motivation. Lincoln, NE : University of Nebraska Press.

Hunus, R., & Fraser, B.J. (1997). Chemistry learning Environment in Brunei Darussalam's
secondary Schools. In D.L. Fisher., & T. Richards. (Eds.), Science, Mathematics and
Technology Education and National Development: Proceedings of the Vietnam conference
(pp.108-120). Hanoi; Vietnam.

Hyde, J. (2005). The gender similarities hypothesis. American Psychologist, 60, 581-592.

Jackson, D., Ahmed,S., & Heapy, N. (1976). Is achievement a unitary construct? Journal of
Research in Personality,10, 1-21.

Jackson, J. (2002). Enhancing self-efficacy and learning performance (motivation and social
processes). The Journal of Experimental Education, 70, 243-255.

Jacobson, L. (2000). Valuing diversity-student-teacher relationships that enhance achievement.
Community College Review, 28, 49-66.

Johnson, L., Lutzow, J., Strothoff, M., & Zannis, C. (1995). Reducing negative behavior by
establishing helping relationships and a community identity program. Rockford Ill.

Joyce, D. (2000). Temperament based learning styles of children with conduct disorder and
oppositional defiant disorder. Dissertation presented at the University ofFlorida.
Gainesville, Fl.

Joyce, D. & Oakland, T. (2005). Temperament differences among children with conduct
disorder and oppositional defiant disorder. The California School Psychologist, 10, 125-
136.

Juarez, A. (2000). Enhancing student performance through classroom motivation. Dissertation
Abstracts (ERIC Document Reproduction Service No. ED458298).

Jung, C. (1971). Psychological types (H. G. Baynes, Trans.). Princeton, NJ: Princeton
University Press. (Original work published 1921).










Kalin, N., Larson, C., Shelton, S., Davidson, R. (1998). Assymetrical frontal brain activity,
cortisol, and behavior associated with fearful temperament in rhesus monkeys. Behavioral
Neuroscience, 112, 286-293.

Kakman, D. (2004). Motivational role of primary students' perceptions of their classroom
experience. Dissertation presented at Northern Illinois University.

Kaplan, (2000). Achievement goals and intergroup relations. In P. Pintrich & D. Schunk (Eds.)
(2nd Ed.) (2002) Motivation in education; theory, research, and application (p. 97-136).
Upper Saddle River, NJ: Merrill Prentice-Hall.

Karnes, F. A., & McGinnis, J. (1994). Correlations among the scores on the Matrix Analogies
Test Short Form and the WISC-R with gifted youth. Psychological Reports, 74, 948-950.

Kaufman, A., & Kaufman, N. (1990). Kaufman Brief Intelligence Test Manual. Circle Pines,
MN: American Guidance Service.

Kaufman, A., McClean,J., & Reynolds, C. (1998). Analysis of WAIS-R factor patterns by sex
and race. Journal of Clinical Psychology, 47, 548-557.

Keirsey, O. & Bates, M. (1984). Please understand me: character and temperament types. (5th
ed.). Del Mar, CA: Prometheus Nemesis Book Company.

Keyser, V., & Barling, J. (1981). Determinants of children's self-efficacy beliefs in an academic
environment. Cognitive Therapy Research, 5, 29-39.

Khine, M., & Fisher, D., (2001). Classroom environments and teachers' cultural background in
secondary science class in an Asian context. Paper presented at the International
Educational Research Conference, University of Notre Dame, Fremantle, Western
Australia.

Khoo, H. & Fraser, B. (1997). Using classroom environment dimensions in the evaluation of
adult computer courses in Singapore. Paper presented at Annual Meeting of the American
Educational Research Association, Chicago. Chicago, IL.

Kim, H., Fisher, D., & Fraser, B. (2000). Classroom environment and teacher interpersonal
behavior in secondary science classes in Korea. Evaluation and Research in Education, 14,
3-22

Kolb, D. (1984). Experiential learning: Experience as a source of learning and development.
Englewood Cliffs, NJ: Prentice-Hall.

Kristal, J. (2005). The Temperament Perspective. Baltimore, MD: Paul H. Brooks Publishing
Co.

Kunc, N. (1992). The need to belong: rediscovering Maslow's hierarchy of needs. In R. Villa, J.
Thousand, W. Stainback, & S.Stainback (Eds.). Restructuring for caring and effective
education. Baltimore, MD: Paul H. Brookes










Lawrence, G. (1982). People types and tiger stripes: a practical guide for learning styles.
(Second edition) Center for application of psychological types: Gainesville, FL.

Learmouth,A., Nadel,L., & Newcombe, N. (1999). Children's use of landmarks: Implications
for modularity theory. Psychological Science, 13, 337-341.

Lee, J. (2002). Racial and ethnic achievement gap trends: reversing progress toward equity?
Educational Researcher, 31, 3-12.

Lee, E. & Bryk, A. (1988). Curriculum tracking as mediating the social distribution of high
school achievement. Sociology of Education, 61, 78-94

Lee, E. & Bryk, A. (1989). A multilevel model of the social distribution of high school
achievement. Sociology of Education, 62, 172-192.

Lee, V. & Smith, J. (1997). High school size: which works best and for whom? Educational
Evaluation and Policy Analysis, 19, 205-227.

Levy, N., Murphy, C., & Carlson, R. (1972). Personality types among Negro college students.
Educational and Psychological Measurement, 32, 641-653.

Lewis, C., Perry, R. & Hurd, J. (2004). A deeper look at lesson study. Educational Leadership,
2, 18-22

Lewis, C., Perry, R., & Murata, A. (2003). Lesson study and teacher knowledge development:
collaborative critique of a research model and methods. Paper presented at the Annual
Meeting of the American Educational Research Association, Chicago, IL.

Little, B., Lecci, L., & Watkinson, B. (1992). Personality and personal projects: Linking Big
Five and PAC units of analysis. Journal of Personality, 60, 501-525.

Lipman, L. & Moore, K. (2005). Why do children need to flourish? Conceptualizing and
measuring indicators of positive development. New York: Springer Publishing.

Logon, H. (1968) Incentive theory, and changes in reward. In G.H. Bower (Ed.) The
psychology of learning and motivation. New York: Academic.

Madden, L. (1997). Motivating students to learn better through own goal setting. Education,
117, 411-414.

Machr, M. & Nicholls, J. (1980). Culture and achievement motivation: A second look. In N.
Warren (Ed.), Studies in cross-cultural psychology (pp. 221-267). New York: Academic

McClelland, D. (1985). How motives, skills, and values determine what people do. American
Psychologist, 40, 812-825.

McClelland, D. (1987). Human motivation. Cambridge, MA: Cambridge University Press.










McClelland, D. & Koestner, R., & Weinberger, J. (1989). How do self-attributed and implicit
motives differ? Psychology Review, 96, 690-702.

McCrae, R., Costa, P., Ostendorf, F., Angleitner, A., Hiebiikova, M., Avia, M., Sanz, J.,
Sanchez-Bernardos, M., Kusdil, M., Woodfield, R., Saunders, P., & Smith, P. (2000).
Nature Over Nurture :Temperament, Personality, and Life Span Development. Journal of
Personality and Social Psychology, 78, 173-186.

McRobbie, C., & Fraser, B. (1993). Associations between student outcomes and psychosocial
science environment. Journal of Educational Research, 87, 78-85.

Meyers, I. & McCaulley, M. (1985). Manual: A guide to the development and use of the Myers-
Briggs Type Indicator. Palo Alto, CA : Consulting Psychologists Press.

Middleton, J. & Spanias, P. (1999). Motivation for achievement in mathematics: findings,
generalizations, and criticisms of the research. Journal for research in Mathematics
Education, 1, 65-88.

Midgley, C. (2002). (Ed.) Goal Structures and patterns of adaptive learning. Hillsdale, NJ:
Lawrence Erlbaum.

Midgley, C., Feldlaufer, H., & Eccles, J.(1989). Student-teacher relations and attitudes toward
mathematics before and after transition to junior high school. Child Development, 60,
981-992.

Midgley, C., Machr, M., Hicks, L., Roeser, R., Urdan, T., Anderman, E., & Kaplan, A. (1996)
Patterns of Adaptive Leamning Survey (PALS). The University of Michigan. Flint, MI.

Moos, R. (1979). Evaluating educational outcomes. San Francisco, CA: Jossey-Bass Publishers.

Moos, R. & Trickett, E. (1974). Classroom environment scale manual. Palo Alto, CA:
Consulting Psychologists Press.

Mormede, P., Courvoisier, H., Ramos, A., Marissal-Arvy, N., Ousova, O., Desautes, C., Duclos,
M., Chaouloff, F., Moisan, M. (2002). Molecular genetic approaches to investigate
individual variations in behavioral and neuroendocrine stress responses.
Psychoneuroendocrinology, 27, 563-5 84.

Myers, I. B. (1962). The Myers-Briggs Type Indicator [manual]. Princeton, NJ: Educational
Testing Service.

Myers, I., McCaulley, M., Quenk, 1., & Hammer, A. (1998). MBTI manual: A guide to the
development and use of the Myers-Briggs Type Indicator (3rd ed.) Palo Alto, CA:
Consulting Psychology Press.

Myhill, D. & Jones, S. (2006). "She doesn't shout at no girls'. Pupils' perceptions of gender
equity in the classroom. Cambridge Joumnal of Education, 36, 99-1 13.










Naglieri, J. (1985). Matrix analogies test-short form. San Antonio, TX: The Psychological
Corporation.

Newman, L. (1985). Hemisphere socialization and Jungian typological evidence for a
relationship. Bulletin of Psychological Type, 10, 2, 13-27.

Nicholls, J. (1992). Students as educational theorists In D. Schunk & J. Meece (eds.) Students
perception of the classroom (p267-286). Hillsdale, NJ: Lawrence Erlbaum.

Oakland, T., Alghorani, A., & Lee, D. (2007). Temperament-Based Learning Styles of
Palestinian and US Children. School Psychology International,28, 110-128

Oakland, T., Glutting, J., & Horton, C. (1996). Student styles questionnaire. San Antonio, TX:
The Psychological Corporation.

Oakland, T. & Lub, L. (2006). Temperament styles of children from the People's Republic of
China and the United States. School Psychology Intemnational, 27, 192-208.

Oakland, T., Joyce, D., Glutting, J., & Horton, C. (2000). Temperament-based learning styles of
male and female students identified as gifted and students not identified as gifted. Gifted
Child Quarterly, 44, 183-189.

Oakland, T., Stafford, M., Horton, C., & Glutting, J. (2001). Temperament and vocational
preferences : age, gender, and racial-ethnic comparisons using the Student Styles
Questionairre. Journal of Career Assessment 9, 297-314.

Osterman, K. (2000). Students' need for belonging in the school community. Review of
Educational Research, 70, 323-367.

Overall, J., & Levin,H., (1978). Correcting for cultural factors in evaluating cultural deficit on
the WAIS. Journal of Clinical Psychology, 34, 910-915.

Pajares, F. & Graham, L. (1999). Self efficacy, motivation constructs, and mathematical
performance of entering middle school students. Contemporary Educational Psychology,
24, 124-139.

Paolo, A., Ward, L., Ryan, J., & Hilmer, C., (1996). Different WAIS-R short forms and their
relation to ethnicity. Personal and Individual Differences, 6, 851-856.

Paris, S., Olson, G. & Stevenson, H. (1983) (Eds.) Learning and motivation in the classroom.
Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Pintrich, P. (2000). The role of goal orientation in self-regulated learning. In M. Boedaerts, P.
Pintrich, & M. Zeidner (Eds.). Handbook of self-regulation: theory, research, and
application (pp.451-502). San Diego, CA: Academic Press.

Pintrich, P. & De Groot, E. (1990). Motivational and self-regulated learning components of
classroom academic performance. Journal of Educational Psychology, 82, 33-40.










Pintrich, P. & De Groot, E. (1994). Classroom and individual differences in early adolescents'
motivation and self-regulated learning, Journal of Early Adolescence, 14, 139-161.

Prewett, P. (1995). A comparison of two screening tests (the Matrix Analogies Test Short Form
and the Kaufman Brief Intelligence Test) with the WISC-III. Psychological Assessment,
7, 69-72

Prewett, P. & Farhney, M. (1994). The concurrent validity of the Matrix Analogies Test Short
Form with the Stanford-Binet: Fourth Edition and KTEA-BF (Academic Achievement).
Psychology in the Schools, 31, 20-25

Proctor, C. (1984). Teacher expectations: a model for school improvement. Elementary School
Journal, 84, 469-481.

Punnett, B. (1986). Goal setting and performance among elementary school students. Journal of
Educational Research 80, 40-42.

Resnick, M., Bearman, P., Blum, R., Bauman, K., Harris, K., Jones, J., Tabor, J., Beuhring, T
Sieving, R., Shew, M., Ireland, M., Bearinger, L. & Udry, J. (1997). Protecting
adolescents from harm. JAMA, 278, 823-832.

Rhodes, C. & Houghton-Hill, S. (2000). The linkage of continuing professional development
and the classroom experience of pupils, barriers perceived by senior managers in some
secondary schools. Journal of In-Service Education, 26, 423-435.

Richman, C., Boelsky, S., Koovand, N., Vacca, J., & West, T. (1997). Racism 102: The
Classroom. Journal of Black Psychology, 23, 378-387.

Riser, J. (1979). Spatial orientation in six month old infants. Child Development, 50, 1078-
1087.

Robinson, R. & Carrington, S. (2002). Professional development for inclusive schooling.
International Journal of Educational Management, 16, 239-247.

Rosenkrantz, P., Vogel, S., Bee, H., Broverman, I. & Broverman, D. (1968). Sex-role
stereotypes and self-concepts among college students. Journal of Consulting and Clinical
Psychology, 32, 287-295.

Rothbart, M., Derryberry, D., Posner, M. (1994). A psychobiological approach to the
development of temperament. In Bates & Wachs (Eds.) Temperament: individual
differences at the interface of biology and behavior. Washington DC: American
Psychological Association.

Rotter, J. (1966). Generalized expectancies for internal versus external control of reinforcement.
Psychological Monograph, 80, 1-28.

Rowe, D. & Plomin, R. (1981). Temperament in early childhood. Journal of Personality
Assessment, 41, 150-156.










Ryan, R. & Deci, A. (2000). Self-determination theory and facilitation of motivation, social
development, and well-being. American Psychologist, 55, 68-78.

Ryan, R., Deci, A. & Grolnick, W. (1995). Autonomy, relatedness, and the self: their relation to
development and psychopathology. In D. Cichetti & D.J. Cohen (Eds.). Developmental
psychopathology Vol 1: Theory and Methods. New York: John Wiley and Sons.

Ryan, J., Glass, L., & Brown, C. (2007). Administration time estimates for Wechsler
Intelligence Scale for Children IV subtest, composites, and short forms. Journal of
Clinical Psychology, 63, 309-318.

Saft, E. & Pianta, R. (2001). Teachers' perceptions of their relationships with students: effects of
child age, gender, and ethnicity of teachers and children. School Psychology Quarterly,
16, 125-141.

Schurr, K., Rubie, V., Palomba, C., Pickerill, B., & Moore, D. (1997). Relationships between
MBTI and selected aspects of Tinto' s model for college attrition. Journal of Psychological
Type, 40, 109-120.

Shelton, J. (1996). Health, stress, and coping. In A.L. Hammer (Ed.), MBTI applications: A
Decade of research on the Myers Briggs Type Indicator (pp. 195-215). Palo Alto, CA:
Consulting Psychologists Press.

Shuttleworth- Edwards, A., Kemp, R., Rust, A., Hartman, N., & Radloff, S. (2004). Cross-
cultural effects on IQ test performance: A review and preliminary normative indications on
Wais-III test Performance. Journal of Clinical and Experimental Neuropsychology, 26,
903-920.

Slate, J., Graham, L., & Bower, J. (1996). Relationships of the WISC-R and K-BIT for an
adolescent clinical sample. Adolescence, 31, 777-782.

Spelke, E. (2005). Sex differences in intrinsic aptitude for mathematics. American Psychologist,
60, 950-958.

Stringfield, S. (1994). A model of elementary school effects'. In Advances in school
effectiveness research and practice. Oxford: Pergamom.

Schunk, D. (1981). Modeling and attributional effects on children's achievement: A self-efficacy
analysis. Journal of Educational Psychology, 73, 93-105

Suedfeld, P. & Epstein, Y. (1970). Where is the "D" in dissonance? Journal of Personality, 71,
39,178-188

Stafford, M. (1994). Validity of the STAR: student styles questionnaire: racial-ethnic
comparisons. Dissertation presented at the University of Texas at Austin. Austin, TX.










Sternberg, G. (1990). Brain and personality: extroversion/introversion in relation to AEEG,
evoked potentials and cerebral blood flow. Unpublished doctoral dissertation, University
of Lund, Sweden.

Stewart, R. & Brendefur, J. (2005). Fusing lesson study and authentic achievement: A model for
teacher collaboration. Phi Delta Kappan, 86, 681-687.

Tomarken, A., Davidson, R., Wheeler, R., & Kinney, L. (1992). Psychometric properties of
resting anterior EEG asymmetry: Temporal stability and internal consistency.
Psychophysiology, 29, 576-592.

Thayer, B. (1996). The Relationship of temperament with respect to age, gender, and
race/ethnicity in children and adolescents. Dissertation presented at the University of
Texas at Austin. Austin, TX.

Thomas, M., & Tall, D. (2001). The Long term cognitive development of symbolic algebra.
International Congress of Mathematical Instruction (ICMI) Working Group Proceedings -
The Future of the Teaching and Learning of Algebra, Melbourne, 2, 590-597.

Thomas, A., Chess, S., & Birch, H. (1968). Temperament and behavior disorders in children.
New York: New York University Press.

Townsend, M., & Hicks, L. (1997). Classroom goal structures, social; satisfaction and the
perceived value of academic tasks. British Journal of Educational Psychology, 67, 1-12.

Trope, Y. (1975). Seeking information about one's own ability as a determinant of choice among
tasks. Journal of Personality and Social Psychology, 32, 1004-1013.

Uguroglu, M. & Walberg, H. (1979). Motivation and achievement: A quantitative synthesis.
American Education Research Journal, 16, 375-389.

Urdan, T. & Machr, M. (1995). Beyond a two-goal theory of motivation and achievement: A
case for social goals. Review of Educational Research, 65, 213-243.

Voyer, D., Voyer, S., & Bryden, M., (1995). Magnitude of sex differences in spatial abilities: A
meta-analysis and consideration of critical variables. Psychological Bulletin, 1 17, 250-
270.

Walberg, H. (1979). (Ed) Education environments and effects. Berkely, CA: McCutchan
Publishing Corporation.

Wang, M., Haertel, G., & Walberg, H. (1993). What helps students learn. Educational
Leadership, 51, 74-76.

Wechsler, D. (2003). Wechsler Intelligence Scale for Children-Fourth Edition: Administrative
and scoring manual. San Antonio, TX : The Psychological Corporation.










Wigfield, A. (1994). Expectancy value theory of achievement motivation: a developmental
perspective. Educational Psychology Review, 6, 49-78.

Wilms, J. (2003a). Student engagement at school: A sense of belonging and participation. Paris:
OECD

Wilms, W. (2003b). Altering the structure and culture of American public schools. Phi Delta
Kappan, 84, 606-615

Wechsler, D. (1991). Wechsler Intelligence Scale for Children--Third Edition. San Antonio,
TX: Psychological Corporation

Weiner, B. (1985). An Attributional theory of motivation and emotion. Psychological Review,
92, 548-573.

Weiner, B. & Kukla, A. (1970). An attributional analysis of achievement motivation. Journal of
Personality and Social Psychology, 15, 1-20.

Wetzel, J., Potter, W. & O'Toole, D. (1982). The Influence of learning and teaching styles on
student attitudes and achievement in the introductory economics course: a case study.
Journal of Economic Education, 13, 33-39.

Whiting, B. & Whiting, J. (1975). Children of sex cultures: a psychocultural analysis. Boston,
MA: Harvard University Press.

Wigfield, A. (1994). Expectancy-Value theory of achievement motivation: a development
perspective. Educational Psychology Review, 6, 49-78.

Wigfield, A., Eccles, J. & Rodriguez, D. (1998). The development of children' s motivation in
school contexts. In a. Iran-Nejad & P. D. Pearson (Eds.), Review of research in education,
23. Washington, DC: American Educational Research Association.

Wilson, M. & Languis, M. (1990). A topographic study of differences in the P300 between
introverts and extroverts. Brain Topography, 2, 369-274.









BIOGRAPHICAL -SKETCH

Susan Davis earned her Bachelor of Science degree for psychology and her Master of Arts

in Education for school psychology from the University of Florida. She received her doctorate

of philosophy degree for school psychology at the University of Florida with specialization in

counseling. Susan completed her internship at Shands at Vista, an inpatient psychiatric facility

serving Florida youth in Gainesville, Fl. Her primary field interests include severe behavioral

and emotional disturbed children and adolescents. Her research interests include effects of

individual characteristics and interpersonal relationships within the learning environments on the

academic and socioemotional well-being of children and adolescents. Additionally, Susan is

interested in pursuing the link between emotional disturbance and students at risk for school drop

out.

Susan has presented at state and national conferences on the effects of classroom climate

on motivation and achievement. She also has designed and began implementation of a model for

a school-home advocacy and information resource (SHAIR) network which advocates to aid

parents in gaining a better understanding of special education needs and interventions.