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EFFECTS OF MOTIVATION, PREFERRED LEARNING STYLES, AND PERCEPTIONS OF
CLASSROOM CLIMATE ON ACHIEVEMENT IN NINTH AND TENTH GRADE MATH
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
O 2007 Susan E. Davis
To My Husband
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
ACKNOWLEDGMENTS .............. ...............4.....
LIST OF TABLES .........._.... ...............7.._.._ ......
LIST OF FIGURES .............. ...............8.....
AB S TRAC T ......_ ................. ............_........9
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....
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
A FORM S ........._.___..... ._ __ ...............77.....
B MI SCELLANEOUS STABLE S ............ ..... .__ ............... 0...
LI ST OF REFERENCE S ............ ..... ._ ............... 1....
BIOGRAPHICAL -SKETCH .............. ...............98....
LIST OF TABLES
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
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
Susan E. Davis
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.
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.
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 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
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
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,
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 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,
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
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, 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
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.
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.
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.
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
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
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
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
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 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.
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
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,
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.
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).
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.
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, 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 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;
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
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
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
Figure 1-1. Relationship of constructs examined in this study
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
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
* 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.
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)?
MATERIALS AND METHODS
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
9th Grade Students
10th Grade Students
Mother's Education Level
Masters Degree or Above
Father' s Education Level
Masters Degree or Above
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,
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,
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
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 &
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-
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
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
Preliminary Analysis of Data
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
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
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
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
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
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
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
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
Task Orientation .015
Extroversion-Introve rsion .006
Teacher Support -.405
Table 3-13. Full model of confluence of variables for the prediction of achievement
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
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
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
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
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
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.
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;
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.
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
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
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
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.
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
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 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.
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.
Your child's participation in this study is completely voluntary. There is no penalty for not
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
Thomas Oakland, PhD, Department of Educational Psychology, University of Florida 392-0723
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...
A-2 Student Assent Form
Student Assent Form
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
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)
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
IQ (percentile) 59.65 29.02 .12
Achievement (z- 1.14 1.05 1.0
Extroversion 33.71 54.12 .10
Thinking 5.08 57.64 .26
Practical .47 59.77 -.02
Organized -25.47 55.17 -.07
Task Orientation 33.70 6.25 -.14
Equity 33.70 7.78 .02
Student 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
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
Mean Composite Score
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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
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.