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A MULTIPLE CASE STUDY OF SCIENCE, TECHNOLOGY, ENGINEERING, AND MATH IN SECONDARY SCHOOL BASED AGRICULTURAL EDUCATION By ERIC STUBBS A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2013
2013 Eric Stubbs
To my support network the family, colleagues, friends and teachers who have been essential to shaping my mind and enabli ng me to become the person I am
4 ACKNOWLEDGMENTS First, I would like to acknowledge the guidance of my adviser Dr. Brian Myers, who was essential in the development of this research The other member of my committee Dr. Kirby Barrick also provided essential guidance on my path to becoming a scholar. he first three Chapter s of this study were drafted had a profound effect on my academic writing ability. I would also like to acknow ledge the contribution of the AG STEM Lab, especially those who provide d feedback and helped analyze the case study proposi tions near the midpoint of the research : Jessica Blythe, Cathy DiBenedetto, Tre Easterly, Seth Heinert, Dr. Myers, and Dr. Katie Stofer. Lastly, I would like to acknowledge the essential role of the participants in this research.
5 TABLE OF CONTENTS pag e ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURE S ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 10 ABSTRACT ................................ ................................ ................................ ................... 11 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 12 History of STEM Education ................................ ................................ ..................... 13 Student Achievement in STEM ................................ ................................ ......... 14 Teacher Efficacy in STEM ................................ ................................ ................ 16 Future Careers in Agriculture ................................ ................................ .................. 17 STEM in Agriculture Classrooms ................................ ................................ ............ 18 Statement of the Problem ................................ ................................ ....................... 20 Purpose of the Study ................................ ................................ .............................. 21 Statement of Objectives ................................ ................................ .......................... 21 Significance of the Study ................................ ................................ ........................ 21 De finition of Terms ................................ ................................ ................................ .. 22 Limitations ................................ ................................ ................................ ............... 23 Assumptions ................................ ................................ ................................ ........... 24 Summary ................................ ................................ ................................ ................ 2 5 2 REVIEW OF LITERATURE ................................ ................................ .................... 26 Theoretical Framework ................................ ................................ ........................... 26 Constructivism ................................ ................................ ................................ .. 27 Learning STEM ................................ ................................ ................................ ....... 30 Presage Variables ................................ ................................ ................................ .. 31 Preparation and Professional Development ................................ ..................... 32 STEM Knowledge and Perceptions ................................ ................................ .. 34 Context Variables ................................ ................................ ................................ ... 36 Perceptions of STEM ................................ ................................ ....................... 36 Student Demographics ................................ ................................ ..................... 37 School and District Support of STEM ................................ ............................... 39 Process Variables ................................ ................................ ................................ ... 39 STEM Integrated Curricula ................................ ................................ ............... 40 Science ................................ ................................ ................................ ............. 40 Technology ................................ ................................ ................................ ....... 41
6 Engineering ................................ ................................ ................................ ...... 42 Math ................................ ................................ ................................ ................. 43 Teaching Method ................................ ................................ .............................. 43 Student Engagement ................................ ................................ ........................ 45 Product Variables ................................ ................................ ................................ .. 46 Student Achievement and STEM Knowledge ................................ ................... 46 Integrated Curricula, Contextualized Learning, and Student Achievement ...... 47 Summary ................................ ................................ ................................ ................ 51 3 RESEARCH METHODS ................................ ................................ ......................... 53 Phenomenological Approach ................................ ................................ .................. 54 Researcher Subjectivity ................................ ................................ .................... 54 Methodology ................................ ................................ ................................ ........... 56 Case Study Propositions ................................ ................................ .................. 57 Selection of Cases ................................ ................................ ........................... 57 Data Collection ................................ ................................ ................................ 58 Data Analysis ................................ ................................ ................................ ... 60 Validation ................................ ................................ ................................ .......... 61 Ethics ................................ ................................ ................................ ................ 63 Summary ................................ ................................ ................................ ................ 63 4 FINDINGS ................................ ................................ ................................ ............... 65 Case Study One: Rural High School ................................ ................................ ....... 66 Physical Description ................................ ................................ ......................... 66 Presage Variables ................................ ................................ ............................ 67 Context Va riables ................................ ................................ ............................. 69 Process Variables ................................ ................................ ............................ 69 Product Variables ................................ ................................ ............................. 72 Case Study Two: Centerpoint High School ................................ ............................. 73 Physical Description ................................ ................................ ......................... 73 Presage Variables ................................ ................................ ............................ 73 Context Variables ................................ ................................ ............................. 74 Process Variables ................................ ................................ ............................ 75 Product Variables ................................ ................................ ............................. 77 Case Study Three: Suburban High School ................................ ............................. 78 Physical Descrip tion ................................ ................................ ......................... 79 Presage Variables ................................ ................................ ............................ 79 Context Variables ................................ ................................ ............................. 80 Process Va riables ................................ ................................ ............................ 81 Product Variables ................................ ................................ ............................. 84 Cross Case Analysis ................................ ................................ ............................... 84 Objectives ................................ ................................ ................................ ......... 84 Presage Variables ................................ ................................ ............................ 86 Context Variables ................................ ................................ ............................. 88 Process Variables ................................ ................................ ............................ 89
7 Product Variables ................................ ................................ ............................. 92 Regarding the Case Study Propositions ................................ ................................ 92 Summary ................................ ................................ ................................ ................ 93 5 IMPLICATIONS ................................ ................................ ................................ ...... 98 Original Case Study Propositions ................................ ................................ ........... 98 Additional Case Study Propositions ................................ ................................ ...... 103 Final Case Study Propositions ................................ ................................ .............. 105 Other Implications ................................ ................................ ................................ 106 Recommendations ................................ ................................ ................................ 108 Summary ................................ ................................ ................................ .............. 109 APPENDIX A OTHER DOCUMENTS ................................ ................................ ......................... 112 B STEM DEGREE PROGRAMS ADDRESSED BY SBAE ................................ ...... 115 C IRB APPROVAL ................................ ................................ ................................ ... 123 LIST OF REFERENCES ................................ ................................ ............................. 124 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 135
8 LIST OF TABLES Table page 4 1 Science and technology integration by case ................................ .......................... 93 4 2 Engineering and math integration by case ................................ ............................. 94 B 1 STEM degree programs addressed by each case and possible to address ........ 115
9 LIST OF FIGURES Figure page 2 1 Conceptual model for the study (adapted from Dunkin & Biddle, 1974). ................ 52 3 1 The multiple case study methodology as illustrated in Yin (2003). ......................... 64 4 1 One of the graphs used to discuss population growth at Centerpoint High ............ 94 4 2 A graph showing population distributions for different countries ............................ 95 4 3 Math formulas and conversions used to estimate the area of land by pacing ........ 95 4 4 A page from the biotechnology lab manual ................................ ............................ 96 4 5 Students analyzed the growth of poinsettias ................................ .......................... 96 4 6 An assessment that included science and mathematics without calculations ........ 97 5 1 A cognitive map of the possible underlying STEM integration process ................ 111 A 1 Tables with data related to agricultural labor and yields that show the effect of technology ................................ ................................ ................................ ........ 112 A 2 An agriscience textbook mentioned science and math integration ...................... 112 A 3 A data and observation journal from a lab at case one ................................ ........ 113 A 4 A technical reading from a UF research manual used in case two ...................... 114
10 LIST OF ABBREVIATIONS AG STEM Science technology, engineering, and math in an agricultural context APLU Association of Public and Land grant Universities CDE Career development events the name of FFA competitions CTE Career and technical education FFA The National FFA Organization NRC National Research Council SBAE School based agricultural education STEM Science, technology, engineering, and math UF University of Florida
11 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science A MULTIPLE CASE STUDY OF SCIENCE, TECHNOLOGY, ENGINEERING, AND MATH IN SECONDARY SCHOOL BASED AGRICULTURAL EDUCATION By Eric Stubbs May 2014 Chair: Brian E. Myers Major: Agriculture Education an d Communication This multiple case study investigated the integration of science, technology, engineering, and math in secondary agricultural education. Observations, interviews, documents, and artifacts provided qualitative data and identified the types of STEM knowledge taught in secondary agricultural education. The methodology was grounded in the interpretive framework and phil osophical assumptions of social constructivism The findings were that the three SBAE programs selected for study introduced students to a variety of STEM knowledge, skills, and careers. Science and technology were consistently integrated curricula but engineering and math concepts were only periodically integrated. This study adds to the body of literature that has suggested that student achievement in STEM is increased by agricultural education. Eight case study propos itions were accepte d while two were useful but not accepted for all cases The cases addressed topics within about 40% of federally approved STEM degree programs Areas for improvement and further research within teacher education, teaching methods, and curriculum resources were id entified. A possible model of STEM integration was postulated.
12 CHAPTER 1 INTRODUCTION Education has sought to improve the lives of students and prepare them for careers and other pursuits of life. Yet, the U.S. has experienced ongoing discontent with student achievement since A Nation at Risk claimed that its educational achievements were b eing exceeded by other countries (Committee on Prospering in the Global Economy of the 21st Century 2007 ; Gardner, 1983 ). That report has led to many attempts to reform education through federal and state legislation, yet student achievement on internatio nal standardized science and mathematics exams has not significantly increased (Gonzalez & Kuenzi, 2012). While preventing a decrease in achievement can be seen as a consolation, students have deserved a more ambitious goal than maintaining the status quo. This has been especially important given the evolution of careers in a digital, knowledge based economy. Many have suggested that careers of the future will require more knowledge and skills related to science, technology, engineering, and math (STEM) ( As sociation of Public and Land Grant Universities [APLU], 2009 ; Committee on Prospering in the Global Economy of the 21st Century 2007 ; National Research Council, 2009 ). Increasing student achievement in STEM will help ensure that students are prepared for a job market that requires sophisticated knowledge and skills School based agricultural education (SBAE), as part of career and technical education (CTE) has had a more clear connection to s pecific careers than regular academic classes Agriculture class es have used inherently interdisciplinary contexts and have involved each of the four STEM subjects. SBAE should help address the stagnation of achievement in STEM (National Research Council, 2009). Furthermore,
13 the APLU (2009) predicted a growing shortage of scientists and professionals in food, agriculture, natural resources, and related sciences because the number of graduates science, mathematics, engineering, and technol History of STEM Education Although it began as SMET education, the phrase STEM education was created in the 1990s and became popular due to the worry that other countries would surpass the U.S. economically due to their investment in STEM (Sanders, 2009). Since the phrase was coined, STEM education has been such an active area that some have referred to the phenomenon as STEMmania Recent historic al milestones in STEM education have included: The Journal of STEM Education was created in 2000 (Journal of STEM Education, 2013). designed to increase the number of students s tudying in STEM fields and/or p. 19 ). Four STEM related federal laws were passed between 2005 and 2007, most notably the America COMPETES Act. Formally known as the America Creating Opportunities to Mea ningfully Promote Excellence in Technology, Education, and Science Act, the law expanded STEM education programs and created new programs with the Department of Energy, Department of Education, and the National Science Foundation (Kuenzi, 2008). The Americ a COMPETES Reauthorization Act was passed in 2010 (Gonzalez & Kuenzi, 2012). various demographic groups, U.S. student performance on international mathematics and science tests, f oreign student enrollments in U.S. institutions of higher education,
14 global STEM education attainment, U.S. STEM teacher quality, and the U.S. STEM research by Josh Brown (2012) c descriptive classroom applications for practicing teachers and in rigourous In 2013, as part of the America COMPETES Reauthorization Act, The National Scien year plan for federal investment and action to improve STEM teaching, increase public engagement and student interest in STEM, better serve underrepresented groups in STEM fields, and improve post secondary STEM education. The plan noted that: In his 2011 State of the Union address, President Obama called for a new effort to prepare 100,000 STEM teachers over the next decade with strong teaching skills and deep content knowledge. The P Technology (PCAST) that teachers need to have enough content knowledge to link STEM to compelling real world issues, model the process of scientific investigat ion, effectively address student misconceptions, and help their students learn to reason and solve problems like mathematicians, scientists and engineers. (p. 18) The emphasis on connecting content knowledge, STEM knowledge, real world issues, and problem solving skills has been a key part of the philosophy of STEM education (Ejiwale, 2012). Student Achievement in STEM While little national data on student achievement in technology or engineering have been gathered, achievement in science and math has been a source of dissatisfaction ever since A Nation at Risk and spurred the interest in STEM education. Based on 2011 school year data, 35% or less of 8 th graders were proficient in math or science, with similar percentages for high school students ( Aud et al., 2011 ). However,
15 student achievement slightly increased in both subjects from 2009 to 2011. Despite this limited progress, concern about achievement has been further validated by other statistics: the U.S. ranked 20 th in the world in the proportion of 24 year olds earning degrees in natural science or engineering between 1970 and 2003, and high school students ranked 28 th in math literacy and 24 th in science literacy on the Program for International Student Assessment (PISA) in 2003 (Kuenzi, 2008). More recently, on the 2009 PISA, U.S. students ranked 24 th in math and 19 th in science (Fleischman, Hopstock, Pelczar, & Shelley, 2010). Due to statistics like these, T he STEM education establishment has long believed STEM education as it should, and has been toiling steadfastly to make improvements. But instead of praising their successes, public the decrease in the number of students pursuing STEM fields, particula rly those from historically underrepresented populations has been widely publicized. (Sanders, 2009, p. 22) However, Gonzalez and Kuenzi (2012) pointed out the progress made in graduate enrollment in science and engineering over the past decade. Overall, e nrollments have increased 35%, with minority enrollments increasing over 50%. Yet, out of all science and engineering degrees awarded globally in 2008, the U.S. only produced 10%. China conferred 23% of these degrees, and the European Union produced 19% (G onzalez & Kuenzi, 2012). Out of all degrees awarded in the U.S. in 2008, only 17% of (Kuenzi, 2008). Prospering in the Global Ec onomy of the 21 st Century 12 education in science, do not seem capable of producing enough students with the knowledge and skills
16 305). Their report, titled Rising Above the Gathering Storm, has become a modern day A Nation at Risk. It related student achievement in STEM to the challenges that will be faced in an era of globalization. The committee argued that STEM will be critical t o ensuring economic well being, public and environmental health, security, new industries, and an improved standard of living. Gonzalez and Kuenzi (2012) also connected increasing student achievement in STEM with positive socioeconomic outcomes: More recen t concerns about scientific and technological literacy in the United States focus on the relationship between STEM education and national prosperity and power. Since World War II, the United States has benefitted from economic and military advances made po ssible, in part, by a highly skilled STEM workforce. However, today the economic and social benefits of scientific thinking and STEM education are widely believed to have broad application for workers in both STEM and non STEM occupations. As such, many co ntemporary policymakers consider widespread STEM literacy, as well as specific STEM expertise, to be critical human capital competencies for a 21st century economy. (p. 1) Teacher Efficacy in STEM In discussing its first goal of improving STEM instruction, The National Council on Science and Technology (2013) referenced much research that has indicated how effective teachers have a striking, positive effect on student achievement. Effective STEM teaching has included following student interests, building on their knowledge and experiences, and providing new experiences that engage them in the processes of science and engineering (National Research Council, 2011). In the best STEM programs, being guided through scientific investigations and engineering design projects has empowered students to develop their own identities as STEM learners. The National Research Council (2011) also suggested that career and technical educators have had an advantage when it comes to STEM teaching because
17 For its part, career and technical education is predicated on the idea of making learning relevant and connecting the content with its making content relevant, and they rely heavily on technology as a tool for eng aging in scientific practices. (p. 19) The desire to create more and more effective STEM educators through teacher education programs and professional development has been expressed by the U.S. Government, CTE organizations, and general education organizat ions (National Council on Science and Technology, 2013). In 2006, 45 programs with the goal of recruiting, retaining, or developing K 12 STEM teachers were fede rally funded (Kuenzi 200 8 ). Future Careers in Agriculture Only about 15% of agricultural emplo yment opportunities between 2010 and 2015 were projected to be in production. About 27% were estimated to be directly in science and engineering and 47% were in business and management (Goecker, Smith, Smith, & Goetz, 2010). These statistics have exemplifi ed the long term trend within the agricultural industry of expanding careers outside of farming and production. Technological innovations have improved agricultural yields and efficiency, while reducing the amount of labor required, which has allowed growt h in the supporting areas of agriculture (Drache, 1996; Shoulders, 2012). Although careers in production agriculture certainly have involved STEM related knowledge, these statistics and other factors have indicated an intensifying need for STEM knowledge i n the agricultural industry. In fact, technology has continued to be one of the most important causes of structural changes in the agricultural industry (Dimitri, Effland, & Conklin, 2005). Agriculture has faced the difficult problem of a growing populatio n combined with environmental limits. With population projections at 9 billion for 2050, food production must significantly increase at the same time it is shrinking its environmental footprint
18 (Foley et al., 2011). Furthermore, The National Research Counc il (2009) pointed out including energy security, national security, human health, and climate change are closely tied to the global food and agriculture enterprise. Academic institutions with programs in agriculture are i n a perfect position to foster the next generation of leaders and professionals needed to address these challenges. (p. 1) Therefore, agricultural education should help create a 21 st century workforce that is able to address social, economic, and environme ntal challenges through STEM. The National Research Council (2009) went as far as suggesting that STEM be changed to science, technology, engineering, agriculture, and math ( STEAM ) STEM in Agriculture Classrooms STEM education has been incorporated into agricultural education programs. In fact, the March April 2013 edition of The Agricultural Education Magazine using agriculture to teach STEM. Traditionally, the scientific method, plant and animal biol ogy, cellular biology, biotechnology, genetics, physical science, chemistry, and soil and water science have been included in agricultural curricula (Rao, 1987). The direct integration of science into SBAE was first called for by the National Research Coun cil in 1988. This drove the development of agriscience curricula, led to agriculture classes that provided science credits, and inspired studies that showed how an agricultural context can improve science learning (Conroy, Dailey, & Shelley Tolbert 2000). Agriculture students may learn about and use agritechnology, plus be required to have sufficient computer skills. Liza Goetz (2012) pointed out the usefulness of 2 dissolved oxygen, and GPS j to aid in plant and animal identification. Lawrence and Rayfield (2012) discussed similar
19 STEM inclusion opportunities when growing a school garden. Lee (2000) suggested that the emerging technologies of precision agriculture and biotechnology are important aspects of SBAE. Engineering (FLDOE) Pathways to Engineering class that could be addressed through agricultural edu cation include: sketching and drawing; taking, recording, and converting problems; biotechnology; and mechanical designs (FLDOE, 2013). Coolman (1992) noted that engineering prese nts possibilities for solving problems, while agriculture provides a quickly increasing number of problems related to production and processing. For mathematics, computations, models, measurements, and data analysis have been incorporated into some curric ula. Percentages, fractions, area, volume, and other aspects of geometry and algebra have been taught through design projects, fertilizer applications, ordering media or feed, making budgets, and more (Ray, 2013). Like with science, significant research in to mathematics in SBAE has also been completed ( Miller & Gliem, 1994 ; Stripling, 2012; Young, Edwards, & Leising, 2006 ). However, less research has addressed the possibilities of enhancing agricultural curricula with technology and engineering, although this has been a problem of STEM education research and action in general ( Coppola & Malyn Smith 2006). Career and technical education, especially SBAE, has been pushed to embrace ment is becoming heavily looked upon by administrators as a way to bring relative meaning to
20 (Haug, 2011, p. 7). Documenting and escalating the STEM content taught within a gricultural and other CTE classes may help administrators, politicians, and the public realize their value. Interestingly, CTE has employed many of the same teaching methods that research has suggested for STEM education. When teaching STEM, Ejiwale (2012) activities that integrate the curriculum to promote hands on and other related experiences that would be needed to help solve problems as they relate to their lture teachers have been employing hands on activities for many years through lab work and supervised agricultural experiences (SAEs). Similarly, motivational activities and real world contexts have been inherent in most CTE curricula. Clearly, though SBAE 1988 p. 8). Indeed, SBAE programs have been so diverse that the philosophy of agricultural education has emphasized the process of learning by doing over the specific content learned (Phipps & Osborne, 19 8 8 ). Statement of the Problem This study addressed the nationwide problem of stagnating secondary student achievement in science, technology, engineering, and mathematics. Many trends have indica ted that agricultural careers will continue to require more STEM related knowledge and skills (APLU, 2009; NRC, 2009), yet little interdisciplinary documentation of STEM in SBAE has occurred. While discipline specific studies on enhancing agricultural curr icula with science or mathematics have provided insight, they have not addressed all areas of this problem. How STEM is included in SBAE, as well as teacher
21 and student perceptions of STEM inclusion, have been further documented by this study. Purpose of t he Study The purpose of this study was to determine processes, perceptions, content, and methods used when teaching STEM in selected Florida secondary school agriculture programs, and then to identify implications concerning how STEM is taught in SBAE thro ugh a multiple case study analysis. In addition, teacher and student perceptions of STEM and its inclusion in SBAE were examined. Statement of Objectives The objectives of the study were to: 1. Identify STEM knowledge and skills taught in SBAE. 2. Identify the processes and methods used to teach STEM knowledge and skills in SBAE. 3. STEM inclusion. 4. relationship between attitu des and other variables. Significance of the Study This study addressed several priorities of the American Association for scientific workforce that addresses 21 st ce ntury challenges, directly motivated this research (Doerfert, 2011). As discussed earlier, improving STEM education within SBAE will be a vital step in providing that type of workforce. Priorities four and five were creating meaningful learning environment s and effective education programs (Doerfert, 2011). This study addressed those by observing teaching in regards to meaningful
22 STEM learning and discussing implications for effective SBAE programs. Priority two was new technologies, practices, and adoption (Doerfert, 2011). This is addressed through documenting the ways teachers incorporate technology related topics and the attitudes that students hold about technology. The results of this research can be directly used by teachers, agricultural teacher educ ators, CTE administrators, and curriculum developers. Agriculture teachers may benefit through deeper understanding of STEM education, but also from the discussion of practices and methods. They may be interested to know how others are incorporating STEM i nto their curriculum and inspired to enhance new parts of their curriculum with from this research. The implications of this multiple case study analysis could help teachers However, the research may be most significant to teacher educators. Not only has STEM integration in three SBAE programs been documented, but perceptions and experiences with STEM integration were also examined. Agriculture teacher educators may be better able to prepare teachers to incorporate STEM after reading this study. Because teacher educators may shape the efficacy of future teachers, this study may have a profoun d impact on their work. CTE administrators may also find this information helpful when encouraging teachers to include more S TEM curricula The identification of barriers to STEM inclusion may allow teacher educators and administrators to remove or minimiz e them whenever possible. Definition of Terms A GRICULTURAL E DUCATION Teaching, learning, and t he study of teaching and learning in agricultural contexts (Barrick, 1989)
23 E NHANCED CURRICULUM A subject specific curriculum that has a second subject added th roughout the curriculum in order to improve achievement in the second subject. An example is enhancing an agricultural curriculum with mathematics (Young, Edwards, Liesing, 2006). P ERCEPTIONS OF STEM A thoughts or feelings regarding their confidence, attitude, and efficacy with STEM. Qualitative data were used to understand teacher and student perceptions of STEM. STEM E DUCATION and learning amo ng any two or more of the STEM subjects, and/or between a This definition emphasized the connections between the STEM subjects themselves as well as other subjects. STEM K NOWLEDGE Information related to science, technology, engineering, or math that is contextual and can be recalled (Sanders, 2009). STEM S KILLS The processes of investigation, problem solving, and design used by scientists, engineers, and mathematicians. STEM ski lls are open ended and interdisciplinary, leading learners to realize that there are multiple ways to solve problems (Ejiwale, 2012). STEM I NTEGRATION When STEM related topics are incorporated into a class by a teacher. Limitations Several limitations were inherent in this research. Transferability was limited by the convenience sample although cases were selected according to the principal of maximum variation While assuming that the agriculture classrooms in this study were similar to ones in other areas of Florida was probably safe, the results cannot be generalized to all Florida schools or other states. An urban SBAE program was n ot included in the study. The different knowledge and perceptions of urban students regarding agriculture (Frick, Birke nholz, Gardner, & Machtmes, 1995) may impact the nature of effective STEM integration. The study involved only veteran teachers, so the implications may have limited transferability to newer teachers. Interviews also have limited transferability, though t hey
24 provide tremendous insight into one person (Ary, Jacobs, & Sorenson, 2010). Subject effects were also possible, as teachers and students may have skewed their answers to questions because they knew the research was related to STEM (Ary, Jacobs, & Soren son, 2010) Time was another maj or constraint. Stake (2013 ) suggested that a research er working full time for about a year could complete a multiple case study. The timeline of this study only allowed for about 15 30 hours of work per week for eight months However, the data tended towards saturation by the end of the time in the field, plus int erviews gave an impression of what teachers did outside of the observation period. Nonetheless, the case reports should be thought of as a slice of life. Knowing all aspects of the cases would never be possible, so the goal of this qualitative research has been to provide accurate, rich descriptions of the data. In addition, only the in school portions of SBAE programs were considered, so extracurricular STEM teachin g and learning through FFA and S upervised A gricultural E xperiences were not documented. biases also created limitations. The section on researcher subjectivi ty in Chapter 3 dealt with this challenge by exposing preconceptions and previous experiences as an educator (Denzin & Lincoln, 2013). Assumptions influenced by many contextual factors, and that those factors could be revealed through interview s (Corbin & Strauss, 200 8 ). Dependability was also assumed, in that the to questions were presumed to be truthful and
25 straightforward The ph ilosophical and epistemological assumptions will be discussed in the theoretical framework section of C hapter 2 Summary Careers involving science, technology, engineering, and math have fueled an increasingly important sector of the American economy that will be necessary to address issues related to agriculture, natural resources, energy, infrastructure, and transportation. Some statistics have indicated that the public education system has begun to improve achievement scores in science and math, but tech nology and engineering education gains have not been consistently documented. Agricultural education has had many opportunities to teach STEM related knowledge and skills that are des irable traits for employers (APLU, 2009 ; NRC, 2009 ). Improving STEM achie vement within agricultural education may help to ensure that agricultural production meets the demands of the future. Chapter 1 described the purpose of this research as addressing the problem of low student achievement in STEM by investigating the ways ST EM was taught in selected SBAE programs in Florida. The objectives of the study included identification of STEM related knowledge and skills taught through agricultural education as well as perceptions of STEM inclusion. The results of this study will have implications relevant to teaching, teacher education, administration, and curriculum development that may help improve teacher efficacy and student achievement in STEM.
26 CHAPTER 2 REVIEW OF LITERATURE The model of teaching and learning described by Dunki n and Biddle (1974) provided the framework used to investigate the variables in this study. Given the problem of stagnating achievement in science, technology, engineering, and mathematics (STEM), the variables chosen for study were teacher STEM knowledge, student and teacher perceptions of STEM, teacher preparation, student demographics, school and district support of STEM, teaching method, STEM integration, student engagement, and student achievement. Literature related to the variables was reviewed to pr ovide the necessary background for an effective investigation of STEM teaching and learning in the context of school based agricultural education (SBAE), with the purpose of describing what and how STEM content is integrated into agricultural classrooms. This qualitative research was undertaken from a constructivist epistemology. Therefore, constructivism also informed this investigation and was used to review literature on learning STEM and teaching method. Theoretical Framework ) model of teachin g and learning proposed that teacher traits, student and community characteristics that teachers cannot control, activities in the classroom, and the outcomes of teaching are the elemental variables involved in education. They were named presage, context, process, and product variables, respectively. The relationship between the four variables is shown in Figure 2 1. The other researchers (Park & Osborne, 2 004; Young, Edwards, & Leising, 2006).
27 This multiple case study analysis involved documentation, observation, and measurement across all four types of variables. For this study, the presage variables were STEM knowledge, perceptions of STEM, preparation, and professional development. The context variables were STEM perceptions of students, demographics of students, and the school and district support of STEM. Demographics included were socioeconomic status, gender, and race. In the classroom, the process v ariables examined were STEM integration, student engagement, and teaching method. The product variables were student achievement and perceptions of STEM, since perceptions may change due to classroom activities. A conceptual model that shows the variables involved in t he study was created (Figure 2 1 ). Literature pertaining to each STEM discipline was reviewed because so few studies have examined all four disciplines. Constructivism Constructivism, even when limited to the field of education, is a broad phi losophical theory and epistemology based on the idea that people construct their own realities. Many famous thinkers on education formulated their ideas from a constructivist point of view, including Lev Vygotsky, Jean Piaget, and John Dewey (Oxford, 1997 listening relationship betwee n teacher and student is replaced by one that is more complex and starting from the constructivist position that the knower is an "actor" rather than a "spectator," Dewey staunchly advocated the use of activity methods in the schoolroom for students are potential knowers, yet
28 traditional schooling forces students into the mold of passive receptacles waiting to have information instilled, instead of allowing them to move about, discuss, experiment, work on communal projects, pursue research outdoors in the fields and indoors in the library and laboratory, and so forth. (Phillips, 1995, p. 11) The emphasis that learning is student driven should be unmistakable. The students must construct their knowledge; it cannot be construc ted by the teacher. Then, what teaching methods are used by constructivists? principles: 1. Using student questions to guide lessons. 2. Accepting student initiation of ideas. 3. Promoting student self regulation and action. 4. 5. Encouraging uses of alternative sources of information. 6. Using open ended questions to encourage elaboration. 7. Encouraging students to identify causes and predict consequences. 8. Asking for student ideas before presenting ideas from the text. 9. Allowing adequate time for reflection and analysis. 10. Facilitating reformulation of ideas given new experiences or evidence. 11. Encouraging social interaction. So, experiences and experiential lea rning have been a vital aspect of constructivism. problems is a characteristic which differentiates education based upon experience from p. 96). Therefore, reflection upon experiences is vital, and social interactions and experiences can elicit learning (Fosnot, 1996 ). The
29 higher cognitive demands of constructivist style lessons can cause frustration in some students, so teachers must use the techniques wisely and for the correct purpose (Perkins, 1999). Doolittle and Camp (1999) wrote a seminal article on constructivism within CTE. They stated that behaviorism has been the underlying theory of CTE since the late 1800s and changes in the 19 (Doolittle & Camp, 1999, para. 58). That these principles may enhance the future of CTE was implied. One reason that constructivism has been especially applicable to in occupational, educational, and computer their own understanding is essential The key factors of constructivist pedagogy listed by Doolittle and Camp (1999) are learning in real world environments, social negotiation and mediation, relevant content and skills, using prior knowledge to understand new material, formative assessment that guides future learning, self awareness and self regulation, teachers as facilitators rather than instructors, and multiple perspectives and representations of content. CTE has been naturally inclined to include several of these factors, especially lea rning in real world environments and social skills. Nevertheless, scholars in the profession have yet to explicitly address the shift from behaviorism to constructivism. The path of reform the profession has followed over recent years places a strain on th e degree to which behaviorist learning theory can adequately describe, explain, and predict the pedagogy needed by career and technical education as we move into the new millennium. It may be that cognitive constructivism will be found to be a better solut ion than behaviorism to serve as the learning theory foundation for career and technical education curriculum and pedagogy. (Doolittle & Camp, 1999, para. 58)
30 Learning STEM Becker and Park (2011) completed a meta analysis of research related to integrative STEM learning that involves two or more of the STEM disciplines. A total of 28 studies was selected and examined E ffect sizes were calculated for each study as well as the overall effect. Although the study was limited by the low number of empirical stud exposed to integrative approaches demonstrated greater achievement in STEM subjects. Integrative approaches provide students with a rich learning context to improve student learni integrating science and technology, either together or with the other disciplines, showed the largest effect sizes. Integration involving math repeatedly showed the smallest effect sizes. However, an integrative approach may also aid students in seeing the real world applications of mathematics in STEM fields, providing additional motivation for future achievement (Becker & Park, 2011). Critical thinking ability has also been shown to be related to l earning STEM disciplines. Specifically, Bitner (1991) gave 101 students the Group Assessment of Logical Thinking, which was found to be a significant predictor of critical thinking abilities and of science and mathematics school grades. The assessment expl ained 29% of the variance in mathematics achievement and 62% of the variance in science achievement. achievement. A two year synthesis study funded by the National Science Fou ndation found that STEM learning increased when teachers were involved in professional learning communities (Fulton & Britton, 2011). The involvement in learning teams helped
31 teachers to use more research reas oning and understanding, and engage students in additional modes of problem solving. But, what research based methods might teachers learn from professional learning communities? Shinn et al. (2003) commented on the importance of cooperative, inquiry based problem based, and contextualized teaching and learning when trying to improve student achievement in mathematics through SBAE. Coincidentally, much of the literature on STEM learning has called for similar methods to be used in STEM education, plus has emphasized the role of teacher as facilitator (Ejiwale, 2012; National Research Council, 2011 ; S anders, 2009 ). These methods have also coincided with the constructivist approach. Further discussion of teaching methods will occur in the teaching methods sec tion. Presage Variables toward teaching and self efficacy are crucial to promoting an enabling learning environment for learners. It is believed that the potential effects of attitude are vital for the nature of commitment and resilience an individual may have. Thus it is crucial to understand and examine the attitude of the STEM educator in order to ascertain how they will adapt to the challenges a STEM program initiative may bring. Th is awareness is necessary because positive STEM educator attitudes will influence classroom strategies used to teach and contribute to the formation of positive learner attitudes. (Ejiwale, 2012, p. 88) The above quote sets the proper tone for the discussi on of literature on the presage variables because teacher properties have been shown to have a strong relationship with student achievement (Darling Hammond, 1999 ). The presage variables under consideration in this study were preparation, STEM knowledge, a nd perceptions of STEM.
32 Preparation and Professional Development Several important studies about the preparation of agriculture teachers in terms of incorporating STEM disciplines have been completed, particularly in science and math. Stripling and Roberts (201 2 a ) conducted a pre experimental, one group, pretest posttest study of how a math enhanced teaching methods course affected the mathematics ability and teaching efficacy of pre service agriculture teachers. The pre service teachers were taught the Nat ional Research Center for Career and Technical enhanced lessons, then had to design a lesson plan that taught two mathematics sub standards. After the lessons were presented to the class, an analysis of coviariance te st rev ealed a significant difference between the groups with the experimental group scoring higher. The effect size was .25, which was categorized as large. However, despite average scores of less than 50% on both the pre and post test, the pre service te achers rated their teaching efficacy and personal mathematics efficacy as moderate to high. No significant changes in efficacy scores were found, although the experience caused personal efficacy to decrease slightly and teaching efficacy to increase slight ly. The results from Stripling and Roberts (201 2 a ) were consis tent with Burton, Daane, and Giese n (200 9 ), who conducted a quasi experiment using a convenience sample of elementary pre service teachers. The experimental group received 20 minutes of mathemat ics enhanced content in each class of a teaching methods course, while the control group received the traditional course. The experimental group scored significantly higher (p = .007) on the Content Knowledge for Teaching Mathematics Measure, which was use d as a pre and posttest. The control group did not score significantly higher (p = .157).
33 In a census study of the 355 agriculture teachers in Florida, Myers and Washburn (2008) found 52% of the teachers were not content with the amount of science they were including in their curricula. Additionally, 53% agreed that their lack of experience in science integration was a barrier. Given that the mean years of experience was 15 (Myers & Washburn, 2008) and the fact that calls for science integration began in the late 19 8 0s ( Dyer & Osborne 199 9 ; Newman & Johnson 1993 ), teachers likely did not receive preparation in an area that was emerging around the same time they completed their preparation. 31) of teacher education programs. Despite the calls for integration of STEM into agricultural curricula, there has still been a research gap that makes it difficult to include in teacher her educators can McGhee and Cheek (1990) found that graduates of the agricultural education program at the University of Florida between 1975 and 1985 indicated that t he amount of technical agriculture courses should be increased, while the amount of pre professional and education coursework should stay the same. Attempting a census, they had an 84% response rate. Concerning professional development, Garton and Chung (1 996) surveyed 37 beginning agriculture teachers in Missouri and had them rank 50 different inservice needs. Integrating science was ranked 6 th using computers in classroom teaching was ranked 9 th and teaching using experiments was ranked 11 th Studies of agriculture teacher professional development oriented towards STEM were not found. However,
34 teachers who participated in a nationwide experiment involving math enhanced CTE curricula rated the professional development sessions as very effective (Stone, Al feld, Pearson, Lewis, & Jenson, 2006). Another study involved a STEM themed summer camp and found that professional development helped teachers overcome barriers and incorporate technology in a way that engaged students (Hayden, Ouyang, Scinski, Olszewski, and Bielefeldt, 2011). In sum, teacher education and professional development programs have begun to train teachers to integrate STEM into their curricula, although more research will enable and improve the process (McGhee & Cheek, 1990; Myers & Dyer, 200 4; Myers & Washburn, 2008; Stripling & Roberts, 201 2 b ). STEM Knowledge and Perceptions Much research in this area has combined the measurement of both STEM knowledge and perceptions of STEM, so these variables have been reviewed in the same section. While enhanced teacher preparation has been shown to help pre service teachers, many agriculture teachers have been shown to need professional development to improve their math knowledge and attitude (Burton, Daane, & Gleason, 200 9 ; Miller & Gliem, 1994; Stripl ing, 2012). Miller and Gliem (1994) conducted an ex post facto study with the purpose of explaining the variance in mathematics ability of agriculture teachers. Years of teaching experience, final college grade point average, and attitude toward mathematic s inclusion were the variables that had a significant relationship to mathematics ability. The number of mathematics courses taken was not significantly related. In a survey of West Virginia agriculture teachers, over 90% of teachers agreed that biotechno logy should be included in agriculture curricula (Boone, Gartin, Boone, &
35 Hughes, 2006). However, only a range of 10 20% of the teachers claimed to have applied knowledge of biotechnology ethics, cloning, genetically modified food, genetic engineering, mic robial biotechnology, electrophoresis, food biotechnology, and environmental biotechnology. Applied knowledge was the highest level of the Likert type scale used on the survey. Over 25% but less than 40% indicated having applied knowledge of more tradition al biotechnology topics, such as growth hormones, hybridization, resistant plant species, and tissue culture. This agreed with the Myers and Washburn (2008) survey discussed earlier. Although 71% of teachers agreed that science integration was necessary, 33.5% admitted insufficient knowledge, and 16.5% were not sure they had enough knowledge. The top three barriers identified by the survey were insufficient time and support, lack of materials, and insufficient funding. However, the researchers pointed out thorough investigation is warranted to determine the legitimacy of these concerns and to lack of resources is merely a convenient excuse for a labor Washburn, 2008). Conroy and Walker (2000) examined the integration of academics into aquaculture classrooms using mixed methods. A random sample of 750 agriculture teachers from the National Association of Agriculture Educators yielded a response rate of 55%. The researchers discussed how integration of academic topics into aquaculture worked best when aquaculture was used as a them e to link instruction among different
36 (Conroy & Walker, 2000, p. 61), emphasizing a gain the importance of STEM knowledge and teacher education programs. Although administrative support increased the A them perceptions was that while teachers see the benefits of STEM inclusion, they may not have the knowledge or experience to consistently incorporate STEM into their curricula. Teachers that h ad positive attitudes about integration were more likely to do so. However, the most common barriers to STEM inclusion as identified by teachers were time, funding, and lack of materials (Boone, Gartin, Boone, & Hughes, 2006; Conroy & Walker, 2000; Miller & Gliem, 1994; Myers & Washburn, 2008). Context Variables The contexts provided by each student as well as the context within which the teacher has operated all have played a large role in determining the outcomes of education (D u nkin & Biddle, 1974). A maturity, and experiences have all been involved in achievement. For the purposes of this study, data have been gathered on perceptions of STEM, socioeconomic status, gender, and race. Perceptions of STEM Perce ptions of STEM were defined in Chapter 1 as what a student thinks or feels about STEM disciplines. Students may have positive or negative attitudes towards the subjects, which often have determined their feelings of confidence or self efficacy. In an analy sis of eight undergraduate introductory algebra classes, a positive correlation was
37 Oty, McArthur, & Clark, 2001). Student attitudes towards mathematics and science were also listed as significant factors on student achievement in the subjects by a study of eighth graders known as the Third International Mathematics and Science Study (TIMSS) (Martin et al., 2000 ; Mullis et al., 2000 ). A positive feedback loop of attitude i ncreasing achievement and achievement improving attitude was described. Weinburgh (1995) conducted a meta analysis of 18 studies representing 6,753 subjects and with more positive attitudes tended to have higher achievement scores. themed science and technology in only a week (Hayd en et al., 2011). Balschweid (200 2 ) used a case study approach to evaluate the effects of using agriculture to contextualize a traditional biology class. A survey with 311 responses was also completed, and over 80% of students reported their perceptions of agriculture changed as a result of the course. Over 90% reported that their understanding of the relationship between science and agriculture increased. Student Demographics A longitudinal, national study from 1988 to 1992 provided insight into the demo graphic trends affecting U.S. education: Findings include: males and females did not differ significantly in the numbers of science and mathematics courses they complete; students from higher socioeconomic status families completed more courses in these su bjects than students from lower socioeconomic status families; Asians completed more courses in math and science than Whites, and Whites completed more courses than Blacks and Hispanics; among students with comparable socioeconomic status, the differences in the number of courses completed between Whites, Blacks, and Hispanics are insignificant; test score increases from the end of the 8th grade to the end
38 of the 12th grade are strongly related to the number of math and science courses students completed in high school; and students who completed more math and science courses show greater achievement score gains during high school, regardless of gender, race ethnicity, and socioeconomic status. (Hoffer Rasinski, & Moore 1995, p. 1) Therefore, socioeconomi c status has been a more important predictor of student achievement than race. Furthermore, Caldas and Bankston (1997) found that the socioeconomic status of a school population as a whole had an effect on individual student achievement in a study of Louis iana schools. As discussed in Chapter 1 achievement gaps between races and socioeconomic classes have remained a troubling issue, especially in STEM (Gonzalez & Kuenzi, 2010). The TIMSS study concluded that a significantly larger number of boys in the U.S have been reaching the upper quarter of mathematics achievement, although the overall achievement distributions were not statistically different (Mullis et al., 2000). In science, boys did score significantly higher internationally and in the U.S. (Marti n et al., analysis also found that boys have more positive attitudes towards science. While some research claims that females are underserved in agricultural education (Trauger, Sach, Barbercheck, Kiernan, Br asier, & Findeis, 2008), several studies of SBAE have found that gender was not a factor in achievement or perceptions of STEM disciplines (Johnson & Wardlow, 2004; Swortzel, Jackson, Taylor, & Deeds, 2003 ). However, concerns about diversity were supported by a national survey of over 9,000 agriculture teachers, who were 78% male and 93.6% white (Camp, Broyles, & Skelton, 2002). Data from 2004 to 2006 showed that demographic change has been slow, with 73% of agriculture teachers at the time being male and 8 8% being white (Kantrovich, 2007).
39 School and District Support of STEM Bidwell and Kasarda (1975) revealed that many factors of school district organization have an effect on student achievement. Principals in Florida public schools consider student achiev ement first and foremost when making funding decisions (Smith & Myers, 2012). Furthermore, their positive perceptions of agriculture programs implied an understanding that SBAE can positively affect student achievement. Hence, d focus on the specific aspects of the program that Dyer and Osborne (1999) surveyed a purposive sample of Illinois guidance counselors and found that in agriculture courses were taught displayed more positive attitudes toward agricultural education and perceived that al to become effective public relations Therefore, administrative support of agriculture programs in general may require that administrators perceive agriculture programs as integrating a high level of STEM. Process Variables Process variables are those that occur in the classroom. In studying STEM in SBAE, the focus was on three variables: STEM inclusion, teaching method, and student engagement. Because the literature on STEM integration was limited, literature on e nhanced curricula in general was also reviewed. Then, literature based on subject specific enhanced curricula was reviewed for each discipline included in STEM.
40 STEM Integrated Curricula Roberson, Flower s and Moore (2000) studied the general concept of ac ademic integration in agricultural education in North Carolina. They compared 32 agriculture teachers who had received grants to support academic integration to 32 teachers who had not. Results of the study indicated that the teachers perceived that both s tudents and teachers benefited from integration. That largest perceived benefit to teachers was instructional relevance. Teachers felt that students benefited through workforce preparation and higher level skill development. Teachers did not indicate that students retained more information as a result of academic integration, though. The barriers identified by the study were time and administrative and financial support. The researchers identified a lack of strong support for vocational and academic integra tion among the teachers, probably due to the barriers listed. Overall, the researchers s & Moore, p. 1). Further discussion of enhance d curricula has been separated into subject specific sections below. Studies on the relationship between enhanced curricula and student achievement were reviewed in the product variable section. Science Thomas Dormody (1993) surveyed a random sample of secondary agriculture teachers, stratified proportionally by state to guarantee proper representation. Courses giving science credit were taught by only 34% of the teachers, although 61.4% received professi onal development related to science teaching methods. Courses providing science credits spanned a broad spectrum, including production, forestry, horticulture, agribusiness, agricultural mechanics and engineering, agricultural processing, and
41 resource mana gement. Based on the survey data, Dormody (1993) pointed out that should not worry A survey of agriculture teachers in Indiana found that 70% of the teachers had attended a workshop on science inclus ion and identified barriers of equipment, funding, and professional development (Balschweid & Thompson, 2002). A similar survey of South Carolinia agriculture teachers identified the exact same three barriers, though 73.1% of respondents agreed that they w ere able to teach integrated biological and physical science concepts in their classes (Layfield, Minor, & Waldvogel, 2001). Greg winners showed that teachers who integrate science perceive multiple benefits to students, including increased student achievement and career preparation. Technology In 1998, a Delphi study involving 82 teachers, supervisors, and teacher educators from the western region of the American Associat ion for Agricultural Education revealed a perception that SBAE does not take advantage of state of the art technology equipment, including computer hardware and softwa re, that is used in surveyed agreed that academics should be integrated into agriculture curriculum. Out of to date curriculum is needed f 42) was rated as the third most important.
42 Engineering A project in Jackson County, Georgia used an agricultural engineering project to integrate math and science with inquiry based and project based activities across several grade lev els (Foutz, Navarro, Hill, Thompson, Miller, & Riddleberger, 2011). Workshop activities helped teachers develop a unit based on the operation of a subdivision that has farming activities that provide food and recreation. The unit was based on a real subdiv ision and former dairy farm that were two miles from one of the schools involved. The project had been ongoing for five years and the researchers school Criterion Ref erenced Competency Test scores and high school end of course In Nevada, eight middle school science teachers volunteered to participate in an engineering education research project (Cantrell Pekcan, Itani, & Velasquez Bryant, 2006). As part of their classes, they taught three modules involving simulation and the design, construction, and testing of a prototype. The modules were assessed using project rubric scores, tests, and interviews. Com pared to the state standardized test scores, achievement gaps on the module assessments were smaller for low socioeconomic status students, blacks, Hispanics, and students with disabilities. However, achievement gaps increased for Native Americans, Asians, and females. The changes may have been due the assessment type, the effects of the engineering modules, or both. The researchers also suggested that females often have less experience with tools and construction, and that might have played a role in thei r below mean assessment scores. Through journals, interviews, and a questionnaire, teachers
43 indicated that their science content knowledge increased, the simulations had a positive impact on the activities, and student engagement was unusually high. Math M iller and Vogelzang (1983) conducted over 400 interviews on mathematics inclusion in SBAE in Iowa. Agriculture teachers, math teachers, principals, students, and parents were included. All of the math concepts included in the study were consistently regard ed as being very important to students studying agriculture. The researchers recommended that SBAE teachers use math teachers as resources and include applied concepts, especially converting units of measure, into their courses. A multi state study that t ested a model of math enhanced CTE curricula found positive outcomes (Stone, Alfeld, & Pearson, 2008). CTE teachers randomly assigned to the experimental group (n = 59) were paired with math teachers to help integrate more mathematics into their curricula, and then were compared to a control group (n = (p. 767), with the treatment expl aining over 35% of the variance in the posttest scores. Furthermore, the authors discussed how attempts to increase the number of math courses or coursework required have not had the expected effect on student achievement. They contended that enhancing CTE courses with mathematics provides the context, experiential learning, and problem solving skills necessary to increase achievement. Teaching Method According to agricultural teachers, the most effective teaching strategies are demonstrations, discussion s, laboratories, projects, contests, using real objects, and
44 super vised experiences ( Myers & Dyer, 2004). Another method shown to be particularly effective in agricultural classrooms has been inquiry based teaching (Thoron & Myers, 2012). Cooperative learning, engaging students in the scientific method, providing tangible rewards, establishing and communicating high expectations, using multiple representations, engaging students in reflection, and scaffolding complex knowledge have all been shown to be effective teaching methods ( Darling Hammond & Bransford, 2007; Marzano, 2007). Researchers have concluded that cognitive learning, including student behaviors involving critical thinking, higher order thinking skills, and problem solving, ought to be occ urring in secondary agricultural education. In addition, various instructional methodologies, including problem solving as a teaching approach, simulation, applied learning activities, integrated curriculums, and laboratory teaching practices, have been te sted and then proffered by researchers to describe and, in some cases, explain relationships between cognitive learning, student achievement, and instructional approach in secondary agricultural education. (Edwards, 2004 p. 234 ) Craig Edwards (2004) also pointed out the agreement between agricultural and general education researchers on these effective strategies. However, Dyer and Osborne (1996) emphasized that student learning styles may impact the effectiveness of a teaching method. Using a purposive sa mple of 16 classrooms and a quasi experimental method, they found that the problem solving method was only significantly more effective than the subject matter method for field neutral learners. Field independent (abstract) and field dependent (concrete) l methods. Von Secker and Lissitz (1999) used hierarchical linear modeling to estimate the effects of three instructional practices suggested by the National Science Education Standards on individual science achievemen t. The practices selected for investigation
45 were lab investigations, increasing emphasis on critical thinking, and reducing teacher centered instruction. A sample of 2,018 students was selected from the National Education Longitudinal Study database. Labor associated with higher achievement overall and with more equitable achievement Von Secker & Lissitz, 1999, p. 1121). Emphasizing critical thinking was not related to d ifferences in achievement, although the researchers admitted that this may have been caused by a failure to find systematic differences in emphasizing critical thinking between schools. Decreasing teacher centered instruction was related to higher achievem ent in science. However, teacher centered instruction was sometimes more effective for low socioeconomic status students who may not have been prepared to work independently during a student centered approach. Student Engagement In a study of introductory undergraduate STEM courses taught at 15 different institutions, findings indicated that students were more engaged when instructors were open to questions (Gasiewski, Eagan Garcia, Hurtado, & Chang, 2012 ). Students that reported they were comfortable seek ing help, attending supplemental instruction sessions, asking questions, and collaborating with other students were more likely to be engaged in class. Similarly, in a quantitative study of 63 fifth and sixth grade classrooms, Reyes, Brackett, Riv ers, Whi te, and Salovey (2012) used multilevel mediation analyses to show a positive relationship between the classroom emotional climate and both student engagement and achievement. Independently, student engagement also had a positive relationship with achieveme nt.
46 Product Variables The product variables examined in this study were student achievement, STEM Knowledge, and perceptions of STEM. Given that literature pertaining to perceptions was reviewed in previous sections, it has not been included in this sect ion. Student Achievement and STEM Knowledge The most readily available way to measure student achievement in STEM has been existing standardized tests in science or math, because national data on technology and engineering achievement have not been gathere d. Therefore, literature related to student achievement in science and math was especially applicable to this study. Chiasson and Burnett (2001) completed a census study of 11 th grade students who completed the state mandated exit examination in Louisiana in 1998. They found that agriscience stu dents were more likely to pass and scored higher on most domains of the science portion of the exam than non agriscience students. Agriscience students outscored the other students in the domains of scientific method biology, and earth science. Non agriscience students scored higher in chemistry and there was no statistical difference in physics. Therefore, they concluded that participation in agriscience courses in Louisiana was beneficial to student achievement in science. Duncan, Ricketts, and Shultz (201 2 ) completed a census at a high school in Georgia that compared the pass/fail rates of seniors in a comprehensive agriscience program (n = 66) versus the seniors not in the program (n = 352). While the agriscience students were more likely to pass the science, language arts, and social studies portions of the exam, they were less likely to pass the mathematics portion than non agriscience students. In a similar study, Ricketts, Duncan, and Peake (2006) compared
47 a p urposive sample of 523 agriscience students from 23 schools in Georgia to state averages. About 78% of the agriculture students passed the Georgia High School Graduation Test, compared to a state average of 68%. In fact, the average score for agriscience s tudents was only 3 points below the average of college prep students. Duncan, & Peake, 20 06). A database with information about 80,000 10 th grade students in Florida was used to examine the effect of CTE coursework and programs on science achievement by Israel, Myers, Lamm, and Galindo Gonzalez (2012). Agriculture, education, health, and STEM were the categories of programs included in the analysis. Hierarchal linear modeling revealed that scores on the science Florida Comprehensive Assessment Test (FCAT) increased as students took more courses in agriculture, health, or STEM programs. These th Gonzalez, 2012). Scores did not increase for students in education programs. Students who took multiple courses in different programs did not expe rience gains as large as those who focused on one program. Integrated Curricula, Contextualized Learning, and Student Achievement Literature on the ways that integrated curricula affect student achievement was reviewed to aid the investigation into how STEM integration in SBAE may affect student the section on teacher perceptions, conclud ed that students who participated in an
48 performance in mathematics and science, and made those areas more relevant for A study of 30 agricultural mechanic s career development event (CDE) teams in that STEM inclusion may also increase student a chievement in aspects of SBAE in some cases. Parr, Edwards, and Leising (2006) investigated the effects that a math enhanced curriculum had on the mathematics achievement of agricultural power and technology students. An experimental post test only control group design was employed with 38 classrooms in Oklahoma for a semester. The experimental treatment of a math enhanced curriculum significantly affected student achievement on a mathematics s d = .83). Furthermore, this effect was generated in only one semester. However, the reasons for the effect may include aspects of the experimental group other than math inclusion, such as teacher professional development and the partnering of agriculture and math teachers. Using data from the same classrooms, Young, Edwards, and Leising (2006) conducted research on how the math enhanced curriculum affected the traditional competencies taught during the agricultural power and technology course. A one way a nalysis of variance found no significant difference between the experimental and control groups on an agricultural mechanics competency exam. Therefore, the enhanced high sch ool agricultural power and technology curriculum and aligned
49 10). This experiment w as one of six replications that were part of a nationwide study of math enhanced CTE curricula. The project was run by the National Research Center for Career and Technical Education and involved 236 CTE teachers, 104 math teachers, and 3,950 students (Sto ne et al., 2006). Membership in the experimental group that received math enhanced lessons was correlated with higher test scores on two out of three mathematics testing programs. The investigators suggested that the third program was not difficult enough to detect the differences between the groups, as indicated by higher (Stone et al., 2006, p. 52). One site did have significantly lower content knowledge scores, though the small n led the researchers to view this result with caution. The study also included a qualitative results section that reported that the teachers in the experimental group al., 2006, p. 59). The following quote described what the researchers discovered through interviews pr ior to the study: While most of the CTE teachers reported addressing the math in their courses in some way, they were not engaged in the identification and mapping of math concepts within CTE curriculum and/or subsequent development of math enhanced CTE le ssons using anything similar to the Math in CTE seven elements. We found the most common approach to be a cursory walk through of the math in lessons, projects, or problem solving scenarios. (Stone et al., 2006 p. 43 )
50 Contrastingly, many agriculture teach ers have systematically incorporated science into their classrooms. Enderlin, Petrea, and Osborne (199 3 ) compared student achievement and thinking skills between an integrated class called Biological Science Applications in Agriculture and a traditional ho rticulture class. Using pre and post test scores, they concluded that students in the integrated class made more significant gains in both agricultural and biological knowledge. However, a different study of science enhanced agriculture curricula found no effect on science achievement test scores (Haynes, Robinson, Edwards, & Key, 2012). Although, the researchers noted small differences favoring the enhanced curricula group that led them to suggest repeating the study for a longer time period. While these studies on enhancing curriculum with a specific subject were found, no literature on STEM enhanced SBAE curriculum that included all four STEM disciplines was found. Another relevant study compared a traditional college algebra course to an experimental g roup that received an interdisciplinary algebra and science course that contextualized the algebra. The study lasted two semesters and used four classes in each group (Elliott, Oty, McArthur, & Clark, 2001). During the first semester, random assignment was used. The second iteration allowed students to choose which course they took. The final exam served to measure achievement, the Watson Glaser Critical Thinking Appraisal was used to measure critical thinking, and a five question Likert type scale determin achievement were found in either semester. Although the students with the contextualized mathematics curriculum scored better on each category of the critical thinking test, only the in ference subscore was statistically significant. The overall score
51 was significantly higher for the experimental group only at the .10 level (p = .0687). The experimental group, however, had better attitudes towards math and rated the class as both more int eresting and practical. The researchers explained that although students The f act that achievement did not decrease with the interdisciplinary curriculum was emphasized as well. According to Boaler (1993), most researchers agreed that real word context increases math achievement. Boaler did mention that a small number of researchers disagreed that it will increase the transfer of skills to outside the classroom. To increase real world relevance, Appelbaum (200 8 ) stated that math teachers have been trying to through cherished notions 43 ). Summary This research was conducted through the lens of constructivism. A theoretical framework was created us variables shown in the conceptual model (Figure 2 1 ) provided the basis on which to review research literature. While literature involving both STEM and SBAE was sparse, much research on individua l STEM disciplines and SBAE has occurred. Teacher preparation has been shown to be helpful in enhancing curricula. Survey data have repeatedly shown that most agriculture teachers perceived STEM enhanced curricula positively, but need more preparation, support, and STEM knowledge. Research on stud ent perceptions has ultimately shown that attitudes towards STEM
52 correlate to achievement. Science integration has been called for since the 1980s and has provided many benefits to SBAE. More recent research has provided important information about mathema tics in SBAE teaching and learning. Enhanced curricula have been a complicated research topic with some conflicting studies. However, the majority of literature reviewed indicated that STEM enhanced curricula and research based teaching methods have the po tential to contextualize learning, improve critical thinking, and increase student achievement. Overall, the review of literature indicated that STEM and SBAE have the potential to positively reinforce each other. Figure 2 1. Conceptual model for the study (adapted from Dunkin & Biddle, 1974).
53 CHAPTER 3 RESEARCH METHODS The purpose and objectives of the study were related to the identification of the processes, methods, and perceptions involved in integrating science, technology, engineering, and math (STEM) in school based agricultural education (SBAE). Dunkin the framework for the research. H owever, a specific theory pertaining to STEM in SBAE has not been developed. Given the lack of ex isting theory and the descriptive nature of the research objectives, qualitative research methods were deemed most appropriate. Qualitative research has been increasingly chosen over quantitative methods when the overall goal g of how people make sense out of their lives, delineate the process (rather than the outcome or product) of meaning making, and describe how Specifically, the methodology used w as multiple case study analysis. and when the focus is on a contemporary phenomenon within some real 1). The general question that drove this study concerned how STEM is taught in SBAE. The teachers and administrators had control over events in the agricultural programs, not the investigator. As described in Chapter 1 STEM ed ucation is a contemporary phenomenon that groups four disciplines that have historically been taught in individual courses. SBAE programs have provided the real life context. The m ultiple case study approach or phenomenon appears
54 in p. 27) and also to use cross case analysis to create theoretical propositions about the phenomenon (Stake, 20 13 ; Yin, 2003). Phenomenological Approach Because the philosophy of phenomenology also underlies qualitative research, some assume that all qualitative research is phenomenological, and certainly in one sense it is. Phenomenology is both a twentieth century school of philosophy associated with Husserl (1970) and a type of qualitative researc h. From the philosophy of phenomenology comes a focus on the experience itself and how experiencing something is transformed into consciousness. (Merriam, 200 9 p. 24) Case studies also have had an inherently phenomenological approach due to the emphasis o topic being studied (Merriam, 200 9 ; Stake, 1978 ). Seidman (2013) identified four themes of phenomenology: the transitory nature of human experience, understanding being subjective and dependent on point of view, lived experience as the source of phenomenological approach . would be to come as close as possible to understanding the true is Researcher Subjectivity Similarly, the experience of the researcher with the phenomenon must be taken bias, but the past experiences of a researcher have also crea are learning how to be more consciously aware of the sources of our subjective judgment not to eliminate them entirely, but to use them so that they enhance rather In th is study, I have
55 referenced my own experiences as an educator. I spent six years teaching two years teaching math and four years as an agriculture educator. In order to acknowledge reflexivity, Merriam (2009) suggested that investigators explain their bi ases, experiences, and worldview. My development as a person has inevitably affected the nature of this research. I am a 29 year old, middle class, white male My father was an engineer, and I began college as an electrical engineering major before switchi ng to mathematics. Because my career began with mathematics teaching, math integration came naturally to me when I became an agriculture teacher. In fact, I found math teaching to be more enjoyable when using applied, agricultural contexts. Contextualizing mathematics and critically discussing mathematical aspects of life were an important part of my math teaching. After becoming an agriculture teacher, I created much of my own curriculum based off of the Environmental Resources framework provided by the F lorida Department of Education. Because I did not participate in SBAE during my primary, secondary, or post secondary education, I did not have a direct model on which to base my program. Therefore, it was much different than a traditional SBAE program. I integrated much more content related to ecology and energy than traditional programs. The program was well funded, and I enjoyed the use of many classroom and lab oratory technolog ies such as an electronics training module and computerized data probes that measured pH, gas levels, temperature, soil moisture, and more. A supportive administration and district played a key role in helping me integrate STEM. For instance, a district CTE administrator encouraged me to incorporate an energy curriculum and suppl ied solar and other equipment. The program also had
56 tissue culture and other biotechnology related equipment. The automotive program at the school partnered with my program on a biodiesel project. The result was an agricultural program that included introd uctions to many different fields, including natural resources, biofuels, solar and wind power, biotechnology, and urban planning. The program was named The Academy of Environmental Engineering. Therefore, my experience as an agriculture educator has been a typical and has given me a unique perspective on STEM integration in SBAE. Agriculture education has much to gain from int egrating STEM, and diverse possibilities for curricula exist. Methodology This case study was descriptive and used a multiple case, em bedded design (Stake, 20 13 ). The embedded units of analysis within the case of an SBAE program were the student s teacher s and curricula Data were gathered on each separately, though also on the interactions between them A visual representation of the multiple case study method is shown in Figure 3 1. As illustrated, case studies should begin with theory development, except for exploratory studies (Yin, 2003). In this research, the theoretical framework in Chapter 2 provided enough of a guide to create theoretical case study propositions and for the study to be considered descriptive rather than exploratory. Also of note in the diagram is the dotted line that has been used to represent a feedback loop. When a case study researcher has found data that cau ses reconsideration of the propositions or selection you risk being accused of distorting or ignoring the discovery, just to accommodate the p.51). This Chapter served as the case study protocol the evolution of which is discussed in the cross case analysis
57 Case Study Propositions The case study propositions were created based on the conceptual model (Figure 2 1 ) and the review of literat ure. These theoretical propositions have guided the investigator might b e tempted to cover everything, p. 23). As mentioned in the introduction, case studies have been used to generalize to theoretical propositions. Creating propositions beforehand to guide a case study has been compared to havi ng hypotheses in an exp eriment (Yin, 2003 ). The original propositions were : 1. STEM integration has a positive effect on student achievement in STEM without decreasing agricultural content knowledge. 2. Effective STEM integration leads to positive student and teacher perceptions of ST EM. 3. Teachers with more STEM knowledge, positive STEM perceptions, and STEM related preparation and professional development will incorporate STEM at higher levels. 4. Teachers use a similar underlying process to integrate STEM. 5. Science will be most integrated into curricula, followed by technology. Math will be integrated to a lesser extent, and engineering will be least integrated. 6. Districts and schools that explicitly support STEM integration will have SBAE programs that integrate STEM at higher levels. 7. Stud ent perceptions of STEM wi ll reflect teacher perceptions. 8. Some units or chapters will have high levels of STEM integration while others will have little. Selection of Cases For this research, a case was defined as the in school portion of a secondary schoo l agricultural education program. Portions of SBAE, such as FFA activities and supervised agricultural experiences, were not used as sources of data if they took place
58 outside of the school day. Selection of cases in multiple case studies should be based o p. 47). That is, cases should be selected either as literal replications that predict similar results or theoretical replications that predict contrasting but relate d results. Whereas sampling logic would require enumeration of the entire population, statistical selection methods, and inferential statistics that make generalizations about the population with a certain level of confidence, replication logic has allowed case study researchers to make generalizations concerning the theoretical framework. The three cases in this study were purposively selected as varied, theoretical replications. Selection was also based on convenience due to travel constraints. Each case was a different variation of the context in which SBAE programs exist. As such, cases had a combination of typical and atypical traits that have been described in detail in each case study report. The population size and density of the community as well as the level of integration of biotechnology provided two variables whose spectrums were well represented by the cases. The cases were named according to their size and density, and each program had a different relationship with biotechnology curricula. Subu rban High School had a biotechnology program that was separate from the SBAE program. Centerpoint High School had a biotechnology track within its agriculture program. Rural High School had no biotechnology track or program, though elements of biotechnolo gy were included in its curriculum. Thus, the cases were selected for maximum variation (Merriam, 2009). Data Collection Data were collected from five sources: direct observations, interviews, documents, archival records, and artifacts These represented a ll the possible case
59 study data sources listed by Yin (2003), except participant observation. The procedure used has been summarized in seven steps: 1. Observe classes once per week for at least three hours, with at least eight observa tions total 2. Interview the teacher two times over the course of the observations 3. During the study, e xamine documents and archival records, including lesson plans, classroom documents, textbooks, and district policy, for STEM related content. 4. Take photos or write descriptions of artifacts with STEM related content, such as student projects or classroom objects. 5. Keep a researcher reflection log and use memos to record ideas. 6. Use advisors and colleagues to shape analysis. 7. Send a preliminary analysis to each teacher, then u se the final interview as member checking. Throughout the study, data collection followed the three principles suggested by Yin (2003): use multiple sources of data, aggregate all data into a case study database, and maintain a chain of evidence. The data collection methods are described in more detail below. In this style of ob servation, the researcher acts naturally rather than attempting to avoid interaction, as in non reactive observation. Continuous monitoring was employed, which involved continuous observations during designated periods of time. Each case was observed for a t least three hours once per week over the course of a school semester. Each observation involved taking detailed notes. Notes concerned variables included in the conceptual map (Figure 2 1) wi th a focus on STEM integration. A two column
60 system was employ ed, with a narrow column on the right where S, T, E, or M was written when an individual STEM discipline was being addressed. While observations provided data filtered through a classroom activities, interviews were used to gather data experiences and the meanings they gave them. The interview technique was based on the in depth, phenomenological structure described by Seidman (2013). A three interview series focused first on history, then on current experience, and then on reflection on the meaning has been an integral aspect of this technique. As suggested by Seidman (2013), interviews generally were spaced about a week apart when possible The interv iew duration was shortened to 30 40 minutes in order to fit withi n the planning time typically allotted to teachers Interview questions were based off of observations and were allowed econstruction of experiences had not been in fluenced by the researcher. Documents were another vital source of data. Textbooks, st udent work, and classroom handouts were examined for STEM related content. Similarly, artifacts involving STEM were documented as they were observed. All data and resear cher notes related to the case study, excluding the researcher reflection log were also entered into the case study database as described in the measures of validation section Data Analysis A strength of the case study approach has been the use of multiple sources of evidence to triangulate (Yin, 2003). Using several sources of evidence has allowed case study researchers to develop converging lines of inquiry that are validated by multiple
61 pi eces of information, and therefore, more convincing. Yin (2003), Stake (20 13 ), Seidman (2013), and Richards (2009) served as guides to analysis. Data were examined for convergence around the case stu dy propositions as well as the propositions that emerged during data collection and analysis. First, this examination happened for each case separately. Then, cross case analysis employed further triangulation to settle upon t he findings To aid in analysis, the interviews were transcribed, the n coded in We ftQDA version 1.0.1 On the observation notes, open coding was used to identify the major themes teachers discussed during interviews as well as when STEM disciplines were addressed Through the process of the constant comparative method (Glaser, 1964 ), a n alytical coding was eventually used to identify the abstract concepts and meanings (Richards, 2009). Validation Challenges to validity and reliability are different for qualitative and quantitative research because they hav e been based on different philosophical assumptions. For this reason, Lincoln and Guba (1985) defined the terms credibility, transferability, dependability, and confirmability as the qualitative versions of internal validity, external validity, reliability and objectivity, respectively. However, many researchers still use the original terms but acknowledge that they take on different meanings for qualitative research (Merriam, 2009; Yin, 2003). The terms have been used interchangeably in this study. The fi rst attempt to ensure credibility, dependability, and confirmability was to complete practice interviews and observations before the case study began. A formative pilot case study, as suggested by Yin (2003), was unnecessary because specific
62 research quest ions had already been developed and the number of cases available for study was limited. In addition, coursework on research methodologies and qualitative research in particular was completed. known strategy to sh ore up the validity because it involves multiple measures of the same phenomenon. Triangulation y meaningful as we can get it, relatively free of our own biases, and not likely to mislead the reader 13 p. 77). The case study database also served to create a chain of evidence or audit trail. This, along with the det ailed case study protocol has increased the dependability (Merriam, 2009 ; Yin, 2003 ). the reliability of the research, just as triangulation is a good test of validity. Finding patterns 2007, p. 48). The patterns discovered during analysis are discussed in the case reports in Chapter 4 Respondent validation was also employed to increase credibility and conf irmability of data (Richards, 2009 ; Seidman, 2013 ). The preliminary analysis was sent to the teachers in order to ensure it accurately reflected their realities. The final interviews were completed through email and phone contact, and the teachers were ask ed to discuss the analysis and improve its accuracy. During analysis, rival explanations and propos itions were sought from the AG STEM Lab, a University of Florida research group This combined two methods of
63 increasing credibility suggested by Merriam (20 09) and Yin (2003): exploring rival explanations and investigator triangulation. Failure to find alternative conclusions has helped ensure credibility. External validity, or transferability, has been addressed by generalizing to theory rather than to a po pulation and by using replication logic in a multiple case study (Yin, 2003). Selecting cases for the maximum variation possible within the geographic area of the study also increased transferability (Merriam, 2009). Ethics This study adhered to basic eth ical pri nciples : Informed consent was obtained from all participants. Participants were able to withdraw at any time without penalty. No unnecessary risk to participants. Benefits outweighed potential risks. Research was conducted by a qualified investigator. In addition, steps were taken to assure the anonymity and confidentiality of both the participants and the cases. Fake names have been created and used alternately with case numbers to obfuscate the identities of the participants. Summary T his investigation into the process of STEM integration in SBAE was qualitative research completed with a phenomenological approach. An embedded, multiple case study methodology was chosen to co mplete a descriptive study. This Chapter has provided a detaile d case study protocol that has explained the data collection process and outlined the data analysis strategies. Theoretical case study propositions were generalizable to
64 p. 10). Data sources included observations, interviews, documents, archival records, and artifacts Many steps have been taken to ensure the validity and reliability of this research Figure 3 1. The multiple case study methodology as illustrated in Yin (2003).
65 CHAPTER 4 FINDINGS The purpose of this study was to determine processes, perceptions, content, and methods involved in teaching STEM in selected Florida secondary school agriculture programs, and then to identify implications concerning how STEM is taught in SBAE Qualitative data were gathered regarding the variables shown in the conceptual model. Theoretical propositions regarding the variables were made, and the data were used to check the validity of the propositions. Triang ulation, the constant comparative method member checking, and discussion with colleagues and committee served to ensure the reliability and accuracy of following analysis. As discussed in Chapter 3 the cases were selected according to the principle of maximum variation. The cases had significant variation in two main areas. First, the communities in which the cases occurred each had different population densities. The cases were named based on thi s trait: Rural High School, Centerpoint High School, and Suburban High School. The second trait that was maximally varied relationship to biotechnology. Other differences between the programs included the number of teachers. Rural High ha d one agriculture teacher, while Centerpoint and Suburban had two. However, the programs had several commonalities. All three programs had veteran teachers who completed agriculture education degrees from the University of Florida and had more than 20 yea rs of experience. Each program had facilities for animal and plant production ample storage area and lab oratory equipment, and active FFA chapters. The classrooms were decorated with awards from subdistrict, district, and state career development events ( CDEs). Details regarding each case have been
66 discussed in the case reports below. Each report has discussed data related to the variables in the conceptual model, which were organized according to the four types of (1974) model of teaching and learning. Then a cross case analysis was completed to explore themes that developed and the veracity of the case study propositions Case Study One : Rural High School The physical description, presage variables, context variab les, process variables, and product variables have been discussed below. Physical Description The first case was a program in a town with a population of less than 500. The town was self described as an agricultural community, and most students lived in the rural area surrounding the town. In the 2000 census, about 13% of families were below the poverty line and 96% of residents responded that they were white The dominant industry o f the area was dairy farming. The agricultural program offered the tracks of animal science and agricultural mechanics. The facilities included a classroom, a large shop with wood and metal working equipment, and a land lab. Plaques and awards from FFA car eer development events (CDEs) and agricultural organizations lined the front and side walls of the classroom. The land lab was used primarily for cattle but also included a pig pen and an irrigated garden area. Two tractors and several implements were used to teach tractor operat ions and maintain the land lab. The classes averaged about 14 students. Because the middle and high schools shared a campus, the middle and high SBAE programs were closely connected and took place in adjacent classrooms.
67 Presage Variables Mr. Olsen was a veteran teacher with about 30 years of experience. His position during this study was at the same high school he attended as a student, which put him in a unique position to describe the changes the agricultural program there has undergone. After obtaining his AA from a community college, Mr. Olsen realized his passion for teaching agriculture while volunteering with FFA CDE teams. He then completed a program for agriculture educators at the University of Florida, receiving his bac While he described enjoyment and satisfaction regarding the program, he felt that being prepared for the some of the realities a first year teacher faces was impossible. These realiti es included disruptive students, the broad ability levels of students and socioeconomic problems. In his words, prepared me for. Another story concerned his first experience with a non English speaking student. After taught at two different schools, received his bef ore he began running the program at Rural High School. Regarding STEM knowledge, Mr. Olsen indicated that his collegiate program prepared him well. His previous experiences working on multiple types of farms and for a construction company also provided hi m with a wide array of skills through which he could apply his knowledge. This was evident in many of the projects he complete d with his students, which often involved design and construction. These projects included
68 building a bandstand for the band progr am, repairing and constructing grills, trailers, and chairs, and a manure slurry project discussed below seen even during initial contact via phone when he said the research should be done at another school if the desire was to see fancy PowerPoints and other instructional technology. However, he went on to describe a project that used an industrial vibrator to separate sand from manure slurry in order to better prepare it for vermiculture. This project exemplified how SBAE programs can partner with a local industry and use STEM skills to solve problems and will be discussed further in the process variables sect ion As indicated by the following quote, Mr. Olsen perceived STEM as being a natural and important aspect of agricultural curriculum: Ag teachers have been addressing STEM for years and years. From what I can see, my lesson plans haven't changed because of STEM. I've been doing STEM my whole life. I just didn't call it STEM. It was part of what we did. It was part of our program, it was part of our curriculum. I didn't go, oh I gotta start throwing more lessons in there on math and more lessons in there o n science. I've been teaching science and math my whole life! I just called it ag science, and ag math, and animal science and plant science and soil science. I guess people need to get in the classrooms and see what we're doing, and then they would unders tand. Yet, this statement also demonstr ated his view that STEM was a new educational phrase necessarily impact his teaching. He went on to discuss how agriculture classes are electives, so students have different expectations than for required academic classes. A tension existed between making classes enjoyable for students and ensuring significant STEM learning. While both were important to Mr. Olsen, building rapport with students and cultivating their interest in agriculture was his first pr iority.
69 Context Variables The students at Rural High School were a cohesive group. Many had family members employed by agricultural operations. The gender break down was about even, though the lack of racial diversity of the community was of course passed onto the classroom. The classes tended to socialize in one large group rather than dividing into cliques. However, at certain times, the class did divide itself by gender especially during activities perceived as gender specific. For instance, the males w ere eager to drive the tractor while a group of females stood farther away and socialized. The students exhibi ted varying perceptions of STEM but always seemed to react positively The district supported STEM through funding an engi neering academy at a nearby school. Mr. Olsen was friends with the engineering teacher and talked with him periodically. Process Variables Mr. Olsen began each day by explaining the activities he had scheduled. Often, he would reflect on previous activitie s or community events during this time as well. Most days involved a hands on activity in the workshop or land lab. The hands on activities were ty pically informal and followed the gradual release format (Fisher & Frey, 2008) The word informal has been used here to describe hands on work involve an experiment, gathering data, the design process, or analysis after the activity. During informal activities, students would not bring materials from the classroom to the workshop or land lab. Classroom activities on the other hand, were usually lec tures with overheads or videos. Book work was usually reserved for days with substitutes.
70 Students were consistently engaged in the classroom. Mr. Olsen clearly excelled at clas workshop or land lab, student engagement was also high. Some activities involved only a few students working at a time under close supervision, though, while the other students were supposed to be observing. An example was the tractor driving day. While one student was driving, another would pick up any stakes the drive r knocked over as he or she drove through the course. The other students socialized in small groups during this time. Mr. Olsen described this down time as important for students in terms of patience and social maturity. STEM was integrated in many ways. As expected, the classes were laden with scie nce. On any given observation, several parts of the lesson or activities were coded as science related. Sometimes the researcher was surprised by how aspects of the agriscience curriculum were related to other diverse subject areas, for instance the analogies between animal reproduction lessons and sex education or tractor oper ations and safe driving Details of agritechnology were often taught, especially those used by the local dairies. Students in the agricultural mechanics class learned about and practiced with tools such as welders, flame cutters, saws, and soldering irons. Each student in the agriscience class learned to drive the tractor, with the top students career development event ( CDE ) The instructional and learning technologies used in the classroom were computers, a project or and an overhead projector While math was integrated in several different ways, the researcher did not witness students performing calculations or operations. While data logs were created for
71 several activities, data analysis was completed through dis cussions about the meaning of the numbers rather than through calculations. Economics provided another mathematical context that was often incorporated into activities. One example was a lecture on soldering that mentioned the increasing price of copper an d its effects. Another integration of math happened during a presentation and lab taught by a guest surface that was arable. The lab was a simulation of different erosion scenarios and had a data and observation journal that involved predicting numerical results and measurement (Figure A 3) One of the most interesting STEM related ac tivities was the aforementioned manure slurry project. S tudents became researchers as they recorded the duration of vibration, measured the amount of sand separated, and considered ways to re engineer an improved system. Observing the project, the researcher had the realization that Mr. g a local farm in a way similar to Extension. Mr. Olsen had described how manure run off from dairies may be related to increasing levels of nitrogen in the springs of the area. So, not only were the students learning about an environmental problem, they w ere working towards a solution with a nearby farm. While Mr. Olsen decided to end the project after a few trials due to the challenges of working with liquid manure, the students clea rly practiced critical thinking, exercised their STEM skills and acted a s researchers In closing, a quote from Mr. Olsen summed up how the agricultural mechanics class related to STEM: Ag mechanics is all STEM. It's just, they don't see it as -we don't call it ag mechanics STEM, we call it ag mechanics and they think that k ids are
72 down here changing oil. Well, there's a whole lot more to it. We do plumbing, we do electrical, we do welding, we do oxygen acetylene, we do brazing, we do woodwork, and it's all the math and science and trigonometry and geometry for designing and building the things we build. In fact, the engineering and design projects often benefitted the school community, as exemplified by the bandstands built for the band program. This quote also revealed that Mr. Olsen felt many outsiders do not understand the STEM intensive nature of agricultural curriculum. Product Variables When asked how student achievement was affected by his program, Mr. Olsen stated: Well, we cover a lot of science. We cover a little bit of math. We do a lot on history, the history of agriculture and history. We do a lot on economics and the importance of agriculture the number 2 industry in the state and the money that it brings in. So, we hit all the curriculums a little bit. We do speeches, so you get your public speaking and leade rship skills. Although this research was oriented towards STEM for the reasons discussed in Chapter 1 this quote served as a remind er of the meaning of an interdisciplinary approach to STEM teaching. The history of science was an important part of Mr. Ols which would translate to high student achievement. The students were able to explain the connections between their agriculture, science, and math classes. They said that the a griculture class motivated them to work hard in other classes. Their knowledge of agritechnologies and ability to work with tools
73 Case Study Two : Centerpoint High School The physical description, p resage variables, context variables, process variables, and product variables are discussed below. Physical Description The second case was located in a fairly rural town of almost 3,000 The d emographic makeup was 73% white, 23% black 3% Hispanic and 1% other races. with about 22% below the poverty line. Like Rural High School, most of the students at Centerpoint lived in the rural area surrounding the community. However, Centerpoint al High School. Local industry was focused around production and processing of a plant crop, hay, and livestock. The agriculture program at Centerpoint employed two teachers and offered students biotechnology, animal science, and horticulture tracks The biotechnology and horticulture tracks culminated with industry certification exams The biotechnology program had recently been established using Race to the Top grant money. Class sizes averaged the smallest here, at about 10 students per class. The facil ities included a workshop, computer lab, biotechnology lab, greenhouse, garden, shade area, and land lab. One of the walls in the workshop had FFA CDE names painted on the walls with the years that the program won them painted beneath, like m any school ath letic programs have done. In the classroom, many awards and plaques were on display. Presage Variables Mr. Greer ha s been teaching at Centerpoint for 15 years, with six years prior experience at a different school. He described his preparatory experience a t the
74 University of Florida positively. He noted a good balance between social and natural science classes In describing his collegiate education, he noted : We had a great hands on ag mechanics section. We poured cement, we made rafters, we welded. I mean I learned a lot and I turned right around my first job and I started utilizing that, but then again I did that with all my classes. I took out my lecture notes for soils, for citrus, animal science I went straight to the college notes and modified them for high school. This quote captured how agricultural education across different levels often involves informal activities meant to develop skills in students by having them complete hands on tasks. Mr. Greer also desc ribed a role playing experience: I can still remember [name redacted] throwing paper at me when I was teaching a lesson about wildlife and it was a very good, eye opening world. It was a good experience, I thoroughly e njoyed it. This quote suggested that the role playing experience prepared Mr. Greer to deal with the stress of disruptive students Mr. Greer perceived STEM as inseparabl e from agricultural curriculum. He viewed technology as the driving force behind chan ge in agriculture and agricultural education. He stated that he disliked math during an interview. Context Variables were split fairly evenly along gender lines. However, more ra cial and socioeconomic diversity was evid ent, though did not fully reflect the demographics of the larger community. Students tended to socialize in small groups Several of the students had familial connections to an agricultural activity, with a few even being employed by an agricultural operation.
75 Whether due to school culture or socioeconomic reasons, classroom management was sometimes a challenge. The students were mo re prone to talking out of turn than in the first case. Student talk about STEM varied from calm confidence to boisterous pronunciations of aversion. The district supp orted STEM by procuring funds that create d the biotechnology program. Process Variables Mr. Greer began classes by entering attendance into his computer, as required by the school. Usually, he would have students finish work from the previous day or start a small assignment during this time. Then, he would discuss the day s agenda, which was always written on the board along with essenti al questions. The teaching methods that the researcher witnessed Mr. Greer employ were lecture, discussion, S ocratic questioning, lab work in groups, informal labs, hands on activities, book work, individual work, and call and response. STEM was heavily and explicitly integrated into the curriculum, and that was often reflected in the essential questions that were written on the board Students in this program worked individually on computers on several occasions. The Agrisc ience Foundations class began by examining the importance of agriculture and a career oriented overview of agricultural industries that even included agricultural engineering careers. The unit was based off of textbook materials and included a discussion c country Students were engaged in a complex discussion of the information pr esented by the graphs shown in Figures 4 1 and 4 2 This was the best example of students
76 activel was observed and was a discussion theme suggested by Brunsell (2012). The biotechnology class had similar discussions and written assignments related to the way biotech innovations have affec ted and will affect civilization. While less mathematical in nature, these discussions clearly had students considering the economic, ethical, environmental, and agricultural effects of science, technology, and engineering. S tudents perform ed mathematical calcu lations several times during the observations. One such activity involved pacing a piece of land, using the number of paces to calculate the dimensions, then calculating the total area of the land in square feet, and finally converting the measuremen t to acres. It was treated as a competition, and most students were clearly motivated to be accurate. Some paced the length and width twice to ensure an accurate count. The writing on the board was photographed as documentation (Figure 4 3). This example c aptured how SBAE programs not only show students a real world use of math, but also engage them k inetically with what often seems to be abstract concepts. As expected, the labs completed by the biotechnology clas s involved STEM through and through. Even an introductory lab of making ice cream, which the researcher had fond memories of from a chemistry class when he was in high school, involved practice with STEM knowledge related to measurement, solutions, melting point, and other properties of matter. DNA extraction, gel electrophoresis, tissue culture, and many other labs were all included in the curriculum binders that were purchased with the lab equipment. A photo of the binder has been provided as Figure 4 4.
77 d around poinsettias. Students, under the guidance of Mr. Greer, were responsible for growing, marketing, and selling the poinsettias. Furthermore, students and the other agriculture classes were exposed to the whole spectrum of the poinsettia industry tha nks to field trips to the production nursery where their plants began their lives and the University of Florida poinsettia research greenhouses. College level science readings out of the U niversity of F lorida poinsettia manual were used in the class and in volved mathematic measurements and units (Figure A 4) Different phases of growth, the effect of environmental variables, and concentration and dilution were important topics for students to understand in order to apply ferti lizers and growth regulators. S tudents measured, graphed, and analyzed poinsettia growth as shown in Figure 4 5. Informal experiments based off of student questions were also a common way that Mr. Greer integrated STEM. For instance, a student asked what would happen to the poinsettias the student set a plant aside, not apply growth regulator, and allow all the students to see the results. These informal experiments seemed to motivate the students to ask more quest ions and develop a sense of being a scientist. Product Variables Mr. Greer reported that he had several interactions with subject area teachers that implied his agriculture class was positively affecting students classes: I know in talking to the teachers in science, biology. They just went over scientific names so that's helping a little bit in foundations. The genus and the species. In biotechnology, if they are in 10th grade when we do DNA oh wow, it just reinforces that material that they're receiving in biology about DNA. And I've heard it from the teacher side: They say, Mr.
78 G reer, did you just do a unit on I say yes, I sure did. Oh, the kids seemed to be engaged, they had a good conversation, they knew a lot mor e background than I initially thou ght, I just want to make sure. A nd I say yes, I just covered that. Math? I like to think that we reinforce because a lot of times I think kids believe math is just I have to know it. I have to go through it. I don't like it. So, we try to show them ways they can use it, like fertilizer percentages, parts per million. Those are algebraic expressions. Those are real. How do I figure this percent out? What's a decimal? And geometry. I struggle with geometry, but we will learn to measure a piece of property by pacing it. And you'll have to know the area of a rectangle, perhaps the area of a triangle, to figure the piece of property. Making our garden, we make it square. Well, we use the Pythagorean theorem. Well, I know its 2 t riangles, but it's essentially the same idea. They never have seen a use for a 2 + b 2 = c 2 They know it, but where can they use it in life? Well, we show them. We can use it to make our garden square. Similarly, several students provided examples of agricu lture lessons that exercised STEM knowledge when prompted. Mr. Greer Socratic questioning perceptions of STEM as they engaged in critical thinking. Mr. Greer also told the s tory of a student who ended up in an AG STEM career after completing his program: I had a young lady who came in and she was as green as a gourd. She really thought milk came from Publix because that's where her Dad worked and he would bring home milk. Mak es sense, right, that's where it comes from. But she went on to a private school down in Lakeland, Florida Southern, got a degree in citrus. Got a great job working at the citrus research facility in Lake Alfred. Case Study Three : Suburban High School The physical description, presage variables, context variables, process variables, and product variables have been discussed below.
79 Physical Description The final case took place in a small city of about 10 ,000 that was in proximity to a large r, more urban are a The racial demographics were 67% white, 29% black 4% Hispanic, and 1% other races. A bout 14% of families were below the poverty line. Important agricultural and natural resource operations around this suburban city included c blueberries, vegetable production, and apiaries The program was called the Academy of Agriscience making the integration of science explicit. The facilities included a classro om, greenhouse, teacher area, land lab, and livestock corralling areas Class sizes were largest here, especially in the introductory Agriscience Foundations class. The mean of the observed class sizes was approximately 24. Some classes wer e in the 30s. Presage Variables Ms. Aiken had 34 years of experience teaching agriculture. She completed her and comp leted it in two years. In describing her undergraduate experience, she noted that a large, lecture based chemistry class was her most difficult class. She had a similar experience with a computer programming class during her graduate work. The two stories involved common student complaints about university STEM classes: 100s of students in one class, boring lectures, little help from teachers, and teachers with heavy accents. She chose to take the computer programming class because she had ore. [She] needed an elective and thought, well, that might be
80 learned in the class to her teaching, the story exemplified how teachers in preparation programs have many curr icular options through which to explore STEM. Ms. Aiken noted that she now taught some of the content from University of technical science classes in her program. She also used agriculture teacher workshops as a source of new lessons and suggeste d that the workshops always contained aspects of STEM. She described workshops that involved biotechnology, emerging pathogens, and environmental issues. Clearly, Ms. Aiken had a high level of STEM knowledge in regards to multiple disciplines within agricu lture Ms. Aiken perceived STEM as an important but difficult aspect of teaching agriculture. Her chief concern regarding teaching STEM was the varying ability level of students, which was particularly a challenge in the introductory Agriscience Foundatio ns class. The class provided students with a science credit, which sometimes caused credit deficient seniors to see the class as an alternative to the traditional science classes they had failed. She felt that STEM education was a new term to describe an o ld phenomenon that agriculture teachers have been working with for quite some time She discussed the many time consuming resp onsibilities of teachers, which was a more acute problem for agriculture teachers She seemed concerned that policies related to STEM education may lead to additional stress and wasted time rather than tangible improvements in teaching. Context Variables The student demographics of this program represented a broad range. Though not obvious, differences in the socioeconomic status of students could be discerned. Most classes had near equal percentages of males and females, though a veterinary
81 assisting class did have an 80% majority of females. The percentage of minorities in the school was not reflected in the agriculture classroom. The students in this program had behavioral patterns similar to the ones at Centerpoint High School Talking and short attention spans presented classroom management challenges. The student perceptions of STEM were extremely student dependent. The researc her asked one of the high achieving students whether her science and math classes helped with her agriculture assignments. She discussed how her biology class did and mentioned genetics. She also noted that algebra helped her with calculating animal feed p rices, projecting an animal s weight and other assignments. The researcher asked her a follow up question about the biotechnology District support of STEM was expressed through the funding of both the agriscience program and the biotechnology program. Process Variables Ms. Aiken began classes by taking attendance. Meanwhile, students usually had to complete a warm up activity that she called primetime. It involved reflective journaling or a written response to a content area question. Beef jerky was also often sold to students at the beginning of classes as a fundraiser. The teaching methods Ms. Aiken use d included lecture, discussion, individual work, group work, guest speakers, and informal lab wo rk. Teaching technologies included a Smartboard, projector, digital overhead camera, and computers. Class sizes were large, especially in the ones observed. The researcher was not able to observe some of the advanced classes that had lower numbers of stude nts. While classroom management of the larger classes was certainly difficult, Ms. Aiken
82 would often call on random students during her lectures to keep students engaged. Keeping students engaged during hands on work in the greenhouse also presented a spat ial challenge as some students would only stay engaged when Ms. Aiken was nearby She described the challenge: If I'm outside with the animals, we have a problem with I've got to keep my eyes on what the animal is doing and you can only have one child rea lly working with an animal at a time, especially if it's the beginning. And so you are watching them, and the rest of the class is supposed to be participating and asking questions and watching. But, I have to be watching them, too. It's kind of like being a bus drive r You need that bus driver to watch the road, and you need somebody in that bus to watch the kids. M ost of the hands on activity was informal lab work in the greenhouse. During that time Ms. Aiken often integrated STEM through questions and d iscussions. For instance, when a student was perplexed by a wilted plant that seemed to be developing a root rot problem due to excess moisture, she asked him to compare the weight of it to a nearby healthy plant. When he realized the wilted plant was heav ier, he connected that to having too much water. The indirect question allowed the student to exercise his scientific observation and problem solving skills. Another example was encouraging two students to take a deformed leaf to a microscope when they ask ed what might be causing the problem. Ms. Aiken also answered some student questions by setting up informal experiments in the greenhouses. During an interview, she discussed some of tific problem solving and graphing. And, then we also use the scales we talk about mass and Furthermore, she helped many students participate in the science fair, had them present their science fair experiments to the
83 class, and led a discussion on improvements that could be made if the experiment was repeated. The researcher learned during an interview that the animal science and veterinary assisting classes involved a significant amount of technology education. The cattle at the school each had radio frequency identification (RFID) to track and log all information regarding the animals. A scanner could be used to pull the data up on a comput er. This was especially relevant given the ongoing discussion of livestock tracking policy and law in the U.S. In Ms. Aikens own words, provided through the member checking process: The program also had a heat watch system to detect standing heat in cows. Sensors were mounted on the tailheads of cows grazing in pastures. When another cow mounted, the sensor would send a signal to a receiver which then sent the date, time, and amount of time the identified cow was mounted to a computer. This technology inc reased the success of conception through artificial insemination without using hormones. In addition, g iving vaccinations provided a context through which students learned about medical technologies and performed mathematical calculations of dosages Stude nts learned that transfer needles could be used to move liquid into the bottle of a freeze dried vaccine and dissolve it. Math was integrated periodically through data gathering and analysis was often completed by calculating percentages or fractions A few students were chosen to help Ms. Aiken with the accounting by writing receipts, logging payments, and calculating totals. Ms. Aiken Word problems related to the costs of supplies and size of vaccine dosages were completed during several different observations. E conomics was also discussed periodically
84 Engineering education in this program was primarily related to genetic engineering. However, the researcher also observed Ms. Aiken se nd a small group to fix a broken irrigation system. The students were responsible for de signing a solution and solving the problem, which is an important engineering process as described earlier Product Variables Ms. Aiken also perceived that her agricult skills. She also explained that extra curricular activities were used to motivate students to achieve: For example, my show team I'll have 25 kids on show team. They can't go and do these activities, they can't get out o f class or get out of school unless they have a good GPA. Especially with show team, I will hold it over their head. I'm not taking them out of school for three days to go to Tampa if they have an F. And the teachers know that. So, we work together sometim es in holding I won't say in holding things over there head to get them to work but we do that a lot. Cross Case Analysis In the cross case analysis, data from all three cases were compared and synthesized. While the individual reports functioned to describe the general nature of each program and its STEM teaching, this section has addressed how the cases related to the objec tives, case study propositions and the new propositions and themes that emerged during the research. Objectives The objectives of the study listed in Chapter 1 were: 1. Identify STEM knowledge and skills taught in SBAE. 2. Identify the processes and methods used to teach STEM knowledge and skills in SBAE.
85 3. inclusion. 4. between attitudes and other variable s. All the objectives were successfully completed. The STEM knowledge and skills taught in the SBAE programs were identified and listed in Table 4 1 and 4 2. During this process, a list of federal STEM designated degree programs was obtained ( U.S. Immigrat ion and Customs Enforcement 2012) and used as another way to document the STEM content of agriculture programs The number of degree programs for which each case provided introductory knowledge was counted. The cases introduced knowledge related to 40 4 6 % of the 424 federally approved STEM degree programs. analysis also indicated that 76% of the STEM degree programs co uld be introduced through SBAE. Table B 1 has shown the analysis. planning and taking advantage of teachable moments with improvised STEM related discussion and questioning. One of the case study propositions was related to the process that underlies STEM integration and has been discussed in Chapter 5 T he teaching met hods that were used to teach STEM included l ecture, discussion, Socratic questioning, collaborative group work, formal lab work, informal lab work, the gradual release method, call and response, and individual practice. Regarding the STEM lesson planning p he planning of it takes so long, it takes a lot of work up front to really plan out a good STEM unit. related discussion and questioning, he said:
86 A natural, opportunistic teacher sees a moment, pounces o n it, and tries to expand on that topic. I'm not the greatest, but I like to think that I planned some and others lend themselves just to the moment. The perceptions of the teachers were documented qualitatively in this Chapter During the interviews, the barriers mentioned by teachers were time, funding, and lesson plan resources. Student perceptions of STEM were also described in this Chapter Analysis of the relationship between student perceptions and other variables was com pleted, and it is discussed in several of the following sections. Presage Variables Concerning preparation, a ll three teachers were veteran teachers with over 20 years of experience. Additionally, all three had been through the agriculture education progra m at the University of Florida the same program through which this research has been completed The teachers described a mixture of technical agriculture, education, science, math, and general education classes. They were taught the importance of teachin g the agriscience. Given that they all had gone through the same teacher education program, the multitude of differences between the programs was striking. Certainly, many factors contributed to these differences, including many variables not included in t As an example, one teacher said role playing prepared him for the realities of classroom management, while another teacher felt it was impossible to prepare fully for those realities. The next presage variable was professional development. Two out of three of the teachers discussed professional development experiences related to AG STEM at significant length. The two teachers indicated that workshops often involved AG STEM niversity of F lorida professors] had some really good
87 development can be an effective means of increasing STEM integration in SBAE. The final two presage variables were teacher knowled ge and perceptions of STEM. STEM knowledge was addressed through a case study proposition so has been discussed in that section. Teacher perceptions of STEM were contextual. While the teachers recognized the importance of STEM, their individual perceptions regarding confidence and ability differed Other commonalities included the perception that technology has had an incredible influence on agriculture and teachers must educate students about it. All the teachers wondered whether STEM was simply a new educ ational phrase that might not gain long term significance. An underlying perception that was identified across all three cases was the worry that too much STEM integration would lose student interest For instance, the teacher in case one suggested that to o much math may lessen rapport with students and discourage them from taking agriculture classes as an elective. While the teacher still integrated math through data collection and the Forestry CDE, calculations were not individually performed by students during the observations or on any assignments that were observed After witnessing calculations and discussions related to math several different times in case two the researcher asked Mr. Greer about the activities because he had mentioned a dislike of m ath during an interview. He then emphasized the importance and beauty of math as a way of knowing and condensing information though nuanced, certainly affected the types and levels of STEM integration in their classrooms
88 Teacher perceptions related to engineering were muddled compared to their perceptions of science, technology, and mathematics Only Mr. Olsen discussed engineering during interviews without prompting and used the word in his classes His program integrated engineering education through the agriculture mechanics class. The perceptions of engineering education evolved as a result of this work and from reading more relevant literature. The two core ideas taught during introductory engineering educ ation have been the engineering design process and the interconnections between STEM and society (Brunsell, 2012). Mr. Olsen continually solutions, and optimizing the design as described in the case report earlier. Integration of engineering has been discussed further in the process variables section. Although not a variable included in the model, an important pattern was evident in the teache levels of students, and each teacher discussed the challenges presented by learning disabilities and poverty unclear. Co ntext Variables Cross case analysis of student demographic variables was unnecessary. In the case reports, demographics of the community were reported individually to provide background information. The demographic variables were also discussed in terms of the selection of cases and maximum variation. Therefore, this section has dealt with the cross case analysis of student perceptions and district support. Student perceptions interacted with the other variables
89 in many ways. The following quote from Ms. Ai ken illustrated that teachers who integrate STEM can experience push back from students who have negative perceptions of STEM: Oh, I had kids yesterday ask me in my ag foundations class, because we were going over a paper they had to do it was all math, it was word problems that dealt with med ications and inventory in a veterinary hospital: M s. Aiken why are we doing math in here? Thi s is an ag class. Well, there are practical reasons. So not only did teacher perceptions influence student perceptions, bu t the student and reactions to STEM integration can affect future STEM integration. Student perceptions also appeared to be related to engagement and achievement during observations as was suggested by the literature review (Mullis et al., 2000; Reyes, Brackett, Ri v ers, White, & Salovey, 2012; Weinburgh, 1995). In each case, district support increased STEM integration through the funding of interdisciplinary CTE programs. Each district had CTE academies that were connected to STEM knowledge and skills. This study documented to STEM related nature of SBAE programs, and the existence of each program indicated some amount of district support for AG STEM. Process Vari ables STEM was integrated in each of the SBAE programs in this study. The curricula of the programs had significant overlap, although they were far from identical. As shown in Tables 4 1 and 4 2, programs differed in the type of STEM knowledge and skills t integration of chemistry through chemical equations and laboratory experiments. Centerpoint also stood out for having the most consistent integration of mathematics.
90 On the other hand, the Suburban High School teacher mentioned physics concepts (weight, mass, density) during an interview that were not documented in other programs. Rural High School addressed many engineering areas through the agricultural mechanics class, whi ch resulted in it having the highest percentage of addressed STEM degree programs in Table B 1. Again, the limitations of the study have not allowed the researcher to rule out that the programs address more STEM areas than documented here. He was only able to document what he saw and heard during the semester spent in the field. Several patterns emerged through examining and coding observation notes. Teachers often used t he internet as a source of lesson plans, some of which integrated STEM. The agednet.com website was used by two of the programs. The other main sources of curricula were teacher created materials, teacher preparation programs, textbooks and the FFA. FFA CDEs were referenced by all three teachers as sources of STEM integration. The Forestry CDE in particular was described as math intensive by two teachers. Textbooks were used regularly only in one case. The other two teachers used the textbooks mainly for substitute days or review. No data indicated that STEM integration lessened the agricult ural content of classes. A surprising STEM related activity that occurred across cases was troubleshooting and repairing broken systems. In each case, broken equipment had presented a challenge that the teachers embraced by involving students in solving t he problem. The following quote captured the mixed s ense of stress and opportunity : So, it depends on a lot of times, it'll depend on what happens with, like my day can change drastically. I can have everything planned out for weeks, exactly what I am goin g to do. But, an animal gets injured, that all stops.
91 We've got to go take care of the animals. Fence breaks? We've got to go take care of that. As an ag teacher, you've got to be very flexible. Irrigation repairs were discussed by Mr. Greer. The teachers used these opportunities to engage students in STEM processes such as problem solving and designing repairs. Another pattern was that the majority of hands on activities in agriculture classrooms were informal. During most hands on activities, students wer e generally not required to record data or observations, reflect on their experiences, or manage complex STEM processes. At times, the teachers simplified activities to ensure they were completed properly. For instance, Mr. Greer taught students about mixt ures, concentrations, and fertilizers. However, when fertilizing plants, he would mix a large batch of liquid fertilizer, which the students would then distribute to each plant. Other patterns with teaching method also existed. All teachers were able to integrate STEM conversationally in an improvised way during discussions and hands on activities. Teachers in two cases used a limited number of methods that did not include collaborative group work in the classroom. However, that may be attributable to the low frequency of the observations. Another pattern was that the teachers and students stated that agriculture classes integrated much more than STEM; they also incorporated history, economics, reading, communication skills, and more. Ms. Aiken also pointe d out how agricultural STEM knowledge can often be applied across subjects : Y ou're talking about STEM Y ou're tal king about science, technology. A ll the processes, whether it s the reproductive tract or the chemicals that we use, progesterone or estradiol, or whatever we may be using, all that comes into play in teaching the students about the functions, reproductive functions, not only in animals, but all this can be related to humans. Importantly, student engagement was high in all three programs. The agr iculture teachers prioritized building rapport and caring relationships. Students were comfortable
92 hands on activities played a role in this, but the SBAE teachers relationship building efforts went above and beyond what the researcher would consider the norm. Sometimes, informal hands on activities allowed lower achieving students to shine: I saw a whole new side of them. They had the ability to work, they had the work ethic and they really enjoyed what they were doing. And that gave us more 1 on 1 time and that's I don't want to say a bond, but a connection that I think as an ag teacher, we need to be doing. And, I realize that I miss that a lot when I have 35 in a class. That's hard. And I think that's what makes us special, too. This suggested that SBAE programs can help motivate lower achieving students, potentially affecting the achievement gap. Product Variables As described in the individual case reports, all three teachers stated that their programs affected student achievement in STEM positively. Furthermore, conversations with students revealed their belief that the agriculture class was improving their STEM abilities. This was also corroborated by Mr. G effect on student achievement in their classrooms. This phenomenon has been further discussed through the case study propositions. The inclusion of perceptions as product variables was appropriate; both student and teacher perceptions evolved over the course of the study. As indicated by student talk, their more positive perceptions of STEM may lead to increased motivation for achievement as well as increased interest in STEM careers. Regarding the Case Study Propos itions Because the case study propositions were an attempt to generalize quali tative data to theory, they dealt with the theoretical implications of this research. Therefore,
93 the discussion of the proposition s was reserved for Chapter 5 Other implications related to the cross case analysis were also discussed. Summary Chapter 4 presented the findi ngs of this multiple case study. Individual case reports provided detailed, narrative descriptions of observations as they pertained to the variables included in the study Physical descriptions and key demographics of the cases were also presented. The voices of participants were incorporated by amply quot ing from interviews and other discourse. Documents provided another source of data that was used for within ca se triangulation. Then, a cross case analysis compared and contrasted the cases. Across case triangulation revealed information about the variables and the relationships between them. The four objectives of the study were reviewed and satisfactorily accomp lished. The theoretical case study propositions and their implications were left to be discussed in Chapter 5 Table 4 1 Science and technology integration by case. Case Science Technology All cases Agriscience; Biology (plant science, animal science); Earth science; Science history; Genetics; Measurement; Nutrition; Scientific method; Science literacy; Soil and water science Agritechnology; B iotechnology ; Computer skills; Hand tools; History of techn ology; Teaching technology 1 Welding; Soldering 2 Chemistry 3 Physics Livestock tracking and breeding ; Digital microscope
94 Table 4 2. Engineering and math integration by case. Ca se Engineering Math All cases Agricultural; Genetic; Problem solving; Troubleshooting Algebra; Calculations; Converting measurements; Decimals, fractions, and percent ages ; Economics; Estimation; Measuremen t ; 1 Design process; Mechanical; Electrical Geometry Trigonometry 2 Geometry 3 Figure 4 1 One of the graphs used to discuss population growth at Centerpoint High.
95 Figure 4 2 A graph showing population distributions for different countries. Figure 4 3. Math formulas and conversions used to estimate the area of land by pacing.
96 Figure 4 4. A pa ge from the biotechnology lab manual. Figure 4 5. Students analyzed the growth of poinsettias
97 Figure 4 6. An assessment that included science and mathematics without calculations.
98 CHAPTER 5 IMPLICATIONS The case reports and cross case analysis revealed much about the teaching of STEM in SBAE and the multi faceted challenges addressed by agriculture teachers. Running a successful secondary agriculture program involved many responsibilities beyond those assumed by teachers of the traditional secon dary subjects. Agriculture teachers often manage the lab equipment of a science teacher, maintain small workshops, run an extra curricular organization, and all while managing a small agricultural operation that usually functions as a small business and ra ises funds (Torres, Kitchel, & Ball, 2010) In discussing the implications and recommendations based off this research, these manifold responsibilities were kept in mind. Original Case Study Propositions The first proposition was that STEM integration has a positive effect on student achievement in STEM without decreasing agricultural content knowledge. As suggested by many sources in the literature review ( Enderlin Petrea, & Osborne, 199 3 ; Parr, Edwards, & Leising, 2006; Roberson, Flower s & Moore 2000; Stone et al., 2006) this was accepted across all three cases. Whether gathered through observation informal student questioning, or interviews with teachers, data suggested that students STEM abilities were being exercised and improved over time. Mr. Greer was even approached by another teacher who asked him if he had recently taught a class about cells after noticing that several of his students were especially engaged and parti cipating at a high level. When the researcher asked students about th e connections between agriculture and other classes, they were able to discuss how many of the activities in the agriculture class demonstrated knowledge they were learning in other classes. This
99 demonstrated the power of experiential learning to cultivate interest and provide motivation (Kolb, 1984). The second proposition was that effective STEM integration would lead to positive student and teacher percep tions of STEM. The data suggested that teacher perceptions impacted STEM integration rather than vic e versa. S tating that positive teacher perceptions of STEM and effective STEM integration occur at the same time This rephrasing has made it clear that a teacher who has pu rely negative feelings toward a STEM subject will incorporate it less in the curriculum. Even if t eacher s dislike a topic, they can integrate it effectively as long as they perceive it as important and useful, as exemplified by Mr. Greer and mathematics. T he ability of teacher STEM perceptions to affect student perceptions was discussed in Chapter 4 and the literature review (Balschweid, 200 2 ; Hayden et al., 2011 ) have been and can be positively influenced by activities t hat show them real world applications of STEM. The rewritten proposition was supported by the cross case analysis. Teachers with more STEM knowledge, positive STEM perceptions, and STEM related preparation and professional development will incorporate STEM at higher levels was the third proposition. Because teacher STEM knowledge and perceptions were addressed by other propositions, this proposition was simplified to: Teachers with more STEM related preparation and professional development will incorporate STEM at higher level s. Evidence of this statement was triangulated in several ways. The teacher from case three noted workshops as an impor tant source of new STEM integrated
100 lessons Case two provided some triangulation because the teacher was required to go to professional development for the new biotechnology track of the program. Furthermore, all three teachers were taught at the University of Florida to emphasi ze science and technology in their curricul a and they clearly followed through. An important implication of this proposition has been that teacher educators can guide the future of STEM integration and prepare teachers to deal with the issues discovered through this research. Storie s told by two of the teachers revealed that the agriculture programs they attended as high school students in the 19 70s did not emphasize the science of agriculture, had almost exclusively male students, and were essentially all hands on, informal learning Progress in these areas was easy to see in the cases of this study. The relative lack of engineering and math education in some programs may be related to the fact that the teachers were not directed to emphasize engineering processes and mathematics lik e they were science and technology Certainly, the call for the integration of science into SBAE curricula that began in the 80s (NRC, 1988; Rao, 1987) has been met through the combined effort s of teachers and teacher educators. The next proposition stated that teachers use a similar underlying process to integrate STEM. This proved to be a complex proposition. Gathering data on it was difficult except by directly asking the teachers about the proces s they used to integrate STEM. The curricula of the teache rs were related to their education, profession al development, perceptions and the curricular framework of the program. The teachers responses hinted at the relationship between these aspects. A conceptual ma p (Figure 5 1 ) shows impression of the process. Because the assumptions about
101 relationships between aspects of the process are mine, the limited number of teachers, and the fact that the model was created after member checking was complete d the model may not be accurate or g eneralizable. It is based primarily on experience as a teacher and secondarily on data from the teacher interviews Further research could clarify the relationship between the variables and what additional variables may be involved. If the STEM integration process has been accurately represented by the model, several implications can be distilled. First, b ecause teacher STEM perceptions and available lesson plans were directly connected to S TEM integration, they could have the greatest impa ct on STEM integration in SBAE. Curriculum frameworks could also specify or modify AG STEM related objectives. Other implications include the importance of the professional development and experience feedback loop as well as the value of pedagogical knowle dge related to STEM teaching Next the proposition concerning the level of integration of the separate STEM disciplines was accurate across all three cases. Science was certainly most integrated into curricula, and it was closely followed by technology. T his should not be surprising for two reasons. First, agriculture is an interdis ciplinary discipline of science. Second, the amount of technology involved in the production of crops has been extremely high However, the cross case analysis showed that m ath and engineering were integrated to a lesser extent. classes, though, science and technology still dominated Overall, t his implied that teacher education programs should focus on math and engineering when dealing with STEM integration.
102 The sixth proposition was: districts and schools that explicitly support STEM integration will have SBAE programs that integrat e STEM at higher levels. T his was suggested in different wa y s described in the findings. Interestingly, i n case one, the teacher mentioned the engineering teacher at a nearby high school as an influence on his teaching. Therefore, programs have the ability to affect each at the district rather than only school level. In case two, the grant funded addition of a biotechnology track within the agriculture program showed the power of a combined district and school effort. The third case exemplified this propos ition as an agriscience program despite the The seventh proposition was rejected. Student perceptions did not always reflect griculture teachers certainly influenced Chapter 4 many other factors came into play. The agriculture teachers were in a unique position to affect students more than other teachers, though, because they often work ed with students for several years. Finally, the proposition regarding the varying level of STEM integration between units was supported in multiple ways. In each program, the amount of science, math, engineering, and technology changed significantly according to the topics covered within units. Clearly, attempting to integrate all STEM disciplines at high levels within all units is not feasible, perhaps mostly due to engineering since it is so specific to designing solutions to problems. However, progress could be made towards a steadier, higher level of integration by each of the teach ers in the study. T he teachers noted that they integrated STEM in different ways each year and often in new areas, which suggested that even veteran teachers make yearly changes in STEM integration
103 Therefore, measures could be taken to ensure future and c urrent teachers of agriculture programs continue this professional development process at a level appropriate to their curricula and skills. Additional Case Study Propositions These propositions evolved as a direct result of observations, interactions with teachers and students, teacher interviews, and suggestions from colleagues. While the original case study propositions were based on the literature review and the t houghts, the propositions in this section were a result of experiences during the research. Therefore, the origins of these propositions and the evidence for them were introduced in Chapter 4 A summary and additional details concerning the new propositions are provided below. Data indicated that agriculture teachers must have a sig nificant amount of STEM knowledge simply to keep a program functioning. The knowledge was exhibited to students by the successful agricultural operations that the teachers ra n as part of their programs. Because the teachers are required to have STEM knowle dge beyond that of a typical teacher, they have a unique ability to impart that knowledge to students. Students not only witnessed the application of the teacher s STEM knowledge, they participated in it through experiential education Each teacher describ ed the importance of the STEM classes taken while completing agricultural education degrees, especially the technical agriscience classes. A clear implication was that STEM classes taken during teacher preparation ha ve been an important source of knowledge and abilities for agriculture teachers. A proposition concerning troubleshooting emerged both during observations and teacher interviews. Like the other propositions, it was also confirmed by the
104 experience. In two cases, the teachers discussed the issue without being can be used as a teachable moment in which the students are motivated because they see that something is not working the way it should. A broken irrigation line, fan belt, or animal feeder may threaten the health or survival of plants and animals Indeed, habit of thinking th e that is necessary to the formation of this habit can easily be given indeed should be given without fail in connection with subjects can be taught through problem s, especially vocational subjects. Though students were observed solving problems in other contexts, troubleshooting problems with equipment and production systems seemed to be an automatic aspect of SBAE that integrated STEM A fter designing a solution fo r a broken system with a class, a teacher could increase the rigor by having students design a whole new system. A nother proposition created during the research was that STEM education within SBAE programs can be improved by formalizing some of the informa l hands on activities. Not only was this triangulated through findings discussed in Chapter 4 the experience as an agriculture teacher also supported it. While managing a small agricultural operation as a means of raising funds for the progra m the focus was successfully growing enough plants to meet sales deadline s, which resulted in much informal greenhouse work This created a disconnection between the formalized plant science lessons and labs completed in the classroom and the informal han ds on activities that were responsible for the production of agricultural goods. A similar
105 struggle was evident in the cases of this study. Digital record books, reflection on hands on work, formalizing hands on activities as labs or engineering processes, and emphasizing the hands and improve this type of experiential education. At the same time, informal work does provide students with relaxed, enjoyable often collaborative experiences that may increase their interest in A G S TEM career areas as well Final Case Study Propo sitions The following is the final set of case study propositions that were suggested to be evident by the triangulation of qualitative data across all thr ee cases in this study : 1. STEM integration has a positive effect on student achievement in STEM without decreasing agricultural content knowledge. 2. Positive teacher perceptions of STEM and effective STEM integration occur at the same time and positively infl 3. Teachers with more STEM related preparation and professional development will incorporate STEM at higher levels. 4. Districts and schools that explicitly support STEM integration will have SBAE programs that integrate STEM at higher levels. 5. The amount of STEM integration varies significantly between units within a program. 6. Agriculture teachers must have a significant amount of STEM knowledge simply to keep a program functioning 7. Troubleshooting broken or dysfu nctional systems can serve as a teachable moment involving STEM knowledge and skills. 8. Improving STEM education within SBAE programs can be achieved by formalizing some of the informal hands on activities. These propositions were evident for some cases but were not triangulated by multiple forms of data across all three cases or had a negative case : 1. Teachers use a similar underlying process to integrate STEM and that process is represented by Figure 5 1.
106 2. Science will be most integrated into curricula, follow ed by technology. Math and engineering will be integrated to a lesser extent. Other Implications Throughout the case study, many other implications were noted. These implications could not be formulated as case study propositions because they were specific to one program or outside the scope of the study. Program connections to the community, teacher rapport with students, textbook issues, the importance of hands on skills and the level of STEM integration have been addressed in this section. At times, perhaps when at their best, secondary school agriculture programs can fulfill a role similar to extension agencies. SBAE programs c an collaborate with their local communities and agribusinesses in ways that improve all the participants. In the case of Rural High School, this occurred by partnering with a local dairy farm and experimenting with processing manure slurry. The extent to w hich this happens and whether or not it should be a goal for certain SBAE programs could be an area for further research. Rapport building and the elective nature of agriculture classes were also issues that played a role in STEM integration. However, the variance across cases made it difficult to create a proposition. Rapport with students was the most listed characteristic of agriculture teachers in a Delphi study completed by Roberts and Dyer (2004). Rapport rest in STEM and motivate them to practice. At the same time, a teacher suggested that too much STEM integration might decrease rapport. Concerning the elective nature of the class, he said: not fun, the kids won't take it. If they won't take it, your program dies real quick. If my program was nothing but work, work, work, really tough stuff, eventually. . [trails off].
107 Research could clarify the methods and level of STEM integration and ho w they interact with student rapport. Of course, a solution to this dilemma could be to require all secondary school students to take an AG STEM that promotes interdisciplinary, critical, and creative thinking, collaboration, hands on skills, and student i nterest. The most common metho ds used by agriculture teachers have historically been lectur e and discussion (Myers & Dyer, 2004 ). This was also observed in this study. Collaborative academic work was seen on a regular basis in only one case although hand s on group work was present in all cases. Many of the effective teaching methods discussed in Marzano (2007) and Dar ling Hammond and Bransford (2007 ) were not documented. The implication was that teacher preparation programs may need to provide teachers wi th more pedagogical knowledge. Agriscience textbooks were present in all cases but only used regularly in one. Agriculture textbook companies have acknowledged the necessity of integrating science, technology, and math (Figure A 2). However, as in two of the cases, most of the mathematics in the textbooks involved the memorization of numbers displays of data (Figure A 1). Textbook companies must integrate more calculations and mathematical operations. Furthermore, engineering knowledge, skills, and careers were not consistently integrated into the textbooks examined. The importance of hands on and experiential learning has a long legacy in education (Dewey, 199 8) and has been especially relevant to STEM education (Ejiwale, 20 12 ). The formalization of common hands on activities in SBAE could increase the level of STEM integration. Of course, varying methods have contributed to student learning, so informal learning also has an important role to play (Hofstein & Rosenfeld,
108 1996). Reflective thinking should be used in tandem with any experiential learning to ai d ) Lastly, the level of STEM integration was explored quantitatively by documenting which federally approved STEM degree programs were and can be addressed. The cases were all clustered around addressing about 40% of the degree programs. The fact that SBAE could address about 76% of the degree programs suggested that other curriculum frameworks are possible. Research could investigate if the 40% level was appropriate and if other programs have been addressing the 36% not seen in these cases. Recommendations For researchers: 1. Each of the case study propositions represent a line of inquiry that could be pursued further. 2. Investigate the relationship STEM integration. 3. Compare and contrast learning in formal and informal hands on laboratory activities in SBAE. 4. Investigate the nature and extent of STEM integration within curriculum frameworks for se condary SBAE 5. Explore STEM teaching and learning in the extra curricular portion of agricultural programs. For teacher educators: 1. Focus on preparing teachers to integrate engineering and mathematics into their curriculum. The engineering design process sho uld be included in agriculture teacher preparation curriculum. 2. Be sure that all future agriculture teachers understand the value of STEM integration and discuss their STEM perceptions. If a course on AG STEM integration is not offered, pre service teachers should learn principles of STEM integration and develop STEM enhanced lesson plans in other courses.
109 For practitioners: 1. Regularly discuss the interconnections between the STEM disciplines and STEM, agriculture, and society. 2. Use diverse teaching methods to integrate STEM in both the classroom and the laboratory. 3. Formalize informal hands on learning using the engineering design process, data collection and analysis, economic calculations, or reflective questioning. 4. Emphasize the engineering within project ba sed learning and troubleshooting. 5. Scale up the activities completed by the program to provide theoretical engineering challenges. For curriculum development: 1. Integrate higher levels of STEM in curriculum frameworks and lesson plans 2. Develop textbooks and l esson plans that integrate STEM at higher and more consistent levels. 3. Develop new curriculum frameworks that focus on different areas of the 76% of STEM degree programs that this study suggested were possible to address through AG STEM. Summary The theore tical propositions with which this multiple case study began evolved through the proce ss of gathering data. Two were accepted, t hree were altered and accepted, two were valuable but not supported by all cases and one was rejected Three new propositions w ere created and accepted. Triangulation of data from both the multiple case study analysis and six years of experience as an educator were used to evaluate the propositions. The implications of the propositions were discussed. Other implic ations that could not be formulated as propositions were also discussed. This research has indicated that student achievement in STEM has been positively affected by AG
110 also positively affected which implied increased motivation and interest in STEM careers. A possible model of STEM integration was postulated and should be investigated through further research. Other areas suggested for future research include d the interplay of STEM integration and student perceptions of STEM, whether agriculture teacher education programs have been addressing engineering and mathematics like they have science and technology, and the interaction between STEM integration and rapport Many of the implications were relevant to the practice of teaching agriculture. Classroom teaching methods should be more diverse and include collaborative academic work. Informal hands on activities should be formalized or followed by reflective discussion. Discussions involving the relationships between AG STEM and soc iety need to happen more often. Agriculture textbook publishers and curriculum developers also have a role to play in improving AG STEM teaching. The history of science has been an important aspect of science education (Matthews, 1994), suggesting that the same holds for STEM. The l eadership, communication and collaboration skills taught through SBAE are also essential for STEM careers, which often involve teamwork ( Ejiwale, 2012)
111 Figure 5 1. A cognitive map of the possible underlying STEM integration process.
112 APPENDIX A OTHER DOCUMENTS Figure A 1 Tables with data related to agricultural labor and yields that show the effect of technology Figure A 2. An agriscience textbook mentioned science and math integration.
113 Figure A 3. A data and observation journal from a lab at case one.
114 Figure A 4. A technical reading from a UF research manual used in case two.
115 APPENDIX B STEM DEGREE PROGRAMS ADDRESSED BY SBAE Table B 1. STEM degree programs addressed by each case and possible to address CIP Code Degree Title Case 1: RHS Case 2: CPHS Case 3: SHS Possible 1 Agroecology and Sustainable Agriculture X 1 Animal Sciences, General X X X X 1 Agricultural Animal Breeding X X X X 1 Animal Health X X X X 1 Animal Nutrition X X X X 1 Dairy Science X X X X 1 Livestock Management X X X X 1 Poultry Science X X X X 1 Animal Sciences, Other. X X X X 1 Food Science X X X X 1 Food Technology and Processing X X X X 1 Food Science and Technology, Other. X X X X 1 Plant Sciences, General X X X X 1 Agronomy and Crop Science X X X X 1 Horticultural Science X X X X 1 Agricultural and Horticultural Plant Breeding X X X X 1 Plant Protection and Integrated Pest Management X X X X 1 Range Science and Management X X X X 1 1 1.1199 Plant Sciences, Other. X X X X 1 1 1.1201 Soil Science and Agronomy, General X X X X 1 1 1.1202 Soil Chemistry and Physics X X X X 1 1 1.1203 Soil Microbiology X X X X 1 1 1.1299 Soil Sciences, Other. X X X X 3 3 3.0101 Natural Resources/Conservation, General. X X X X 3 3 3.0103 Environmental Studies. X X X X 3 3 3.0104 Environmental Science X X X X 3 3 3.0199 Natural Resources Conservation and Research, Other. X X X X 3 3 3.0205 Water, Wetlands, and Marine Resources Management. X X X X 3 Forest Sciences and Biology X X X X 3 3 3.0508 Urban Forestry. X 3 3 3.0509 Wood Science and Wood Products/Pulp and Paper Technology X X X X 3 3 3.0601 Wildlife, Fish and Wildlands Science and Management. X X X X 4 4 4.0902 Architectural and Building Sciences/Technology. X 9 9 9.0702 Digital Communication and Media/Multimedia X X X 10 10 10.0304 Animation, Interactive Technology, Video Graphics and Special Effects X 11 11 11.0101 Computer and Information Sciences, General X 11 11 11.0102 Artificial Intelligence 11 11 11.0103 Information Technology X X X X 11 11 11.0104 Informatics X 11 11 11.0199 Computer and Information Sciences, Other. 11 11 11.0201 Computer Programming/Programmer, General 11 11 11.0202 Computer Programming, Specific Applications 11 11 11.0203 Computer Programming, Vendor/Product Certification 11 11 11.0299 Computer Programming, Other. 11 11 11.0301 Data Processing and Data Processing Technology/Technician X X X X 11 11 11.0401 Information Science/Studies X 11 11 11.0501 Computer Systems Analysis/Analyst 11 11 11.0701 Computer Science 11 11 11.0801 Web Page, Digital/Multimedia and Information Resources Design X 11 11 11.0802 Data Modeling/Warehousing and Database Administration X X X 11 11 11.0803 Computer Graphics 11 11 11.0804 Modeling, Virtual Environments and Simulation X 11 11 11.0899 Computer Software and Media Applications, Other. X X X X 11 11 11.0901 Computer Systems Networking and Telecommunications
116 11 11 11.1001 Network and System Administration/Administrator 11 11 11.1002 System, Networking, and LAN/WAN Management/Manager 11 11 11.1003 Computer and Information Systems Security/Information Assurance 11 11 11.1004 Web/Multimedia Management and Webmaster X 11 11 11.1005 Information Technology Project Management Computer Support Specialist 11 11 11.1099 Computer/Information Technology Services Administration and Management, Other. 13 13 13.0501 Educational/Instructional Technology. X X X X 13 13 13.0601 Educational Evaluation and Research. X 13 13 13.0603 Educational Statistics and Research Methods X 14 14 14.0101 Engineering, General X X X 14 14 14.0102 Pre Engineering X X X X 14 14 14.0201 Aerospace, Aeronautical and Astronautical/Space Engineering 14 14 14.0301 Agricultural Engineering X X X X 14 14 14.0401 Architectural Engineering X 14 14 14.0501 Bioengineering and Biomedical Engineering X X X X 14 14 14.0601 Ceramic Sciences and Engineering X 14 14 14.0701 Chemical Engineering X X X X 14 14 14.0702 Chemical and Biomolecular Engineering X X X X 14 14 14.0799 Chemical Engineering, Other. X X X X 14 14 14.0801 Civil Engineering, General 14 14 14.0802 Geotechnical and Geoenvironmental Engineering X 14 14 14.0803 Structural Engineering X X 14 14 14.0804 Transportation and Highway Engineering 14 14 14.0805 Water Resources Engineering X X X X 14 14 14.0899 Civil Engineering, Other. 14 14 14.0901 Computer Engineering, General 14 14 14.0902 Computer Hardware Engineering 14 14 14.0903 Computer Software Engineering X 14 14 14.0999 Computer Engineering, Other. 14 14 14.1001 Electrical and Electronics Engineering X X 14 14 14.1003 Laser and Optical Engineering 14 14 14.1004 Telecommunications Engineering 14 14 14.1099 Electrical, Electronics and Communications Engineering, Other. X X 14 14 14.1101 Engineering Mechanics X X 14 14 14.1201 Engineering Physics/Applied Physics X X 14 14 14.1301 Engineering Science X X X X 14 Environmental/Environmental Health Engineering X X X X 14 14 14.1801 Materials Engineering X X 14 14 14.1901 Mechanical Engineering X X 14 14 14.2001 Metallurgical Engineering X X X 14 14 14.2101 Mining and Mineral Engineering X X X X 14 14 14.2201 Naval Architecture and Marine Engineering 14 14 14.2301 Nuclear Engineering X 14 14 14.2401 Ocean Engineering X 14 14 14.2501 Petroleum Engineering X 14 14 14.2701 Systems Engineering X 14 14 14.2801 Textile Sciences and Engineering X 14 14 14.3201 Polymer/Plastics Engineering X 14 14 14.3301 Construction Engineering X X 14 14 14.3401 Forest Engineering X X X X 14 14 14.3501 Industrial Engineering X 14 14 14.3601 Manufacturing Engineering X 14 14 14.3701 Operations Research X 14 14 14.3801 Surveying Engineering X X X X 14 14 14.3901 Geological/Geophysical Engineering X 14 14 14.4001 Paper Science and Engineering X X X X 14 14 14.4101 Electromechanical Engineering 14 14 14.4201 Mechatronics, Robotics, and Automation Engineering 14 14 14.4301 Biochemical Engineering X X 14 14 14.4401 Engineering Chemistry X X 14 14 14.4501 Biological/Biosystems Engineering X X X X
117 14 14 14.9999 Engineering, Other. X X X X 15 15 15.0000 Engineering Technology, General X 15 15 15.0101 Architectural Engineering Technology/Technician 15 15 15.0201 Civil Engineering Technology/Technician 15 15 15.0303 Electrical, Electronic and Communications Engineering X 15 Technology/Technician X X X X 15 15 15.0304 Laser and Optical Technology/Technician 15 Telecommunications Technology/Technician 15 15 15.0306 Integrated Circuit Design X 15 15 15.0399 Electrical and Electronic Engineering Technologies/Technicians, Other. X 15 15 15.0401 Biomedical Technology/Technician 15 15 15.0403 Electromechanical Technology/Electromechanical Engineering X 15 Technology 15 15 15.0404 Instrumentation Technology/Technician X X X X 15 15 15.0405 Robotics Technology/Technician 15 15 15.0406 Automation Engineer Technology/Technician 15 15 15.0499 Electromechanical and Instrumentation and Maintenance Technologies/Technicians, Other. 15 15 15.0501 Heating, Ventilation, Air Conditioning and Refrigeration Engineering 15 Technology/Technician X X X X 15 15 15.0503 Energy Management and Systems Technology/Technician X 15 15 15.0505 Solar Energy Technology/Technician. X 15 15 15.0506 Water Quality and Wastewater Treatment Management and Recycling X X X X 15 15 15.0507 Environmental Engineering Technology/Environmental Technology X X X 15 15 15.0508 Hazardous Materials Management and Waste Technology/Technician X X X X 15 15 15.0599 Environmental Control Technologies/Technicians, Other. X X X X 15 15 15.0607 Plastics and Polymer Engineering Technology/Technician X 15 15 15.0611 Metallurgical Technology/Technician X X 15 15 15.0612 Industrial Technology/Technician X X X X 15 15 15.0613 Manufacturing Engineering Technology/Technician X 15 15 15.0614 Welding Engineering Technology/Technician X X 15 15 15.0615 Chemical Engineering Technology/Technician X X 15 15 15.0616 Semiconductor Manufacturing Technology 15 15 15.0699 Industrial Production Technologies/Technicians, Other. X X X X 15 15 15.0701 Occupational Safety and Health Technology/Technician X X X X 15 15 15.0702 Quality Control Technology/Technician X X X X 15 15 15.0703 Industrial Safety Technology/Technician 15 15 15.0704 Hazardous Materials Information Systems Technology/Technician 15 15 15.0799 Quality Control and Safety Technologies/Technicians, Other. 15 Aeronautical/Aerospace Engineering Technology/Technician 15 15 15.0803 Automotive Engineering Technology/Technician X X 15 15 15.0805 Mechanical Engineering/Mechanical Technology/Technician X X 15 15 15.0899 Mechanical Engineering Related Technologies/Technicians, Other. X X 15 15 15.0901 Mining Technology/Technician X 15 15 15.0903 Petroleum Technology/Technician X 15 15 15.0999 Mining and Petroleum Technologies/Technicians, Other. X 15 15 15.1001 Construction Engineering Technology/Technician X X 15 15 15.1102 Surveying Technology/Surveying X X X X 15 15 15.1103 Hydraulics and Fluid Power Technology/Technician X X 15 15 15.1199 Engineering Related Technologies, Other. X X X X 15 15 15.1201 Computer Engineering Technology/Technician 15 15 15.1202 Computer Technology/Computer Systems Technology X X X 15 15 15.1203 Computer Hardware Technology/Technician X X 15 15 15.1204 Computer Software Technology/Technician X 15 15 15.1299 Computer Engineering Technologies/Technicians, Other. X 15 15 15.1301 Drafting and Design Technology/Technician, General X 15 15 15.1302 CAD/CADD Drafting and/or Design Technology/Technician X 15 15 15.1303 Architectural Drafting and Architectural CAD/CADD X 15 15 15.1304 Civil Drafting and Civil Engineering CAD/CADD X 15 15 15.1305 Electrical/Electronics Drafting and Electrical/Electronics CAD/CADD X
118 15 15 15.1306 Mechanical Drafting and Mechanical Drafting CAD/CADD X 15 15 15.1399 Drafting/Design Engineering Technologies/Technicians, Other. X 15 15 15.1401 Nuclear Engineering Technology/Technician X 15 15 15.1501 Engineering/Industrial Management X 15 15 15.1502 Engineering Design X X 15 15 15.1503 Packaging Science X 15 15 15.1599 Engineering Related Fields, Other. X 15 15 15.1601 Nanotechnology X 15 15 15.9999 Engineering Technologies and Engineering Related Fields, Other. X 26 26 26.0101 Biology/Biological Sciences, General X X X X 26 26 26.0102 Biomedical Sciences, General X X X X 26 Biochemistry X X X X 26 26 26.0203 Biophysics X 26 26 26.0204 Molecular Biology X X X X 26 26 26.0205 Molecular Biochemistry X X X X 26 26 26.0206 Molecular Biophysics X 26 26 26.0207 Structural Biology X X X X 26 26 26.0208 Photobiology X X X X 26 26 26.0209 Radiation Biology/Radiobiology 26 26 26.0210 Biochemistry and Molecular Biology X X X X 26 26 26.0299 Biochemistry, Biophysics and Molecular Biology, Other. X X X X 26 26 26.0301 Botany/Plant Biology X X X X 26 26 26.0305 Plant Pathology/Phytopathology X X X X 26 26 26.0307 Plant Physiology X X X X 26 26 26.0308 Plant Molecular Biology X X X X 26 26 26.0399 Botany/Plant Biology, Other. X X X X 26 26 26.0401 Cell/Cellular Biology and Histology X X X X 26 26 26.0403 Anatomy X X X X 26 26 26.0404 Developmental Biology and Embryology X X X X 26 26 26.0406 Cell/Cellular and Molecular Biology X X X X 26 26 26.0407 Cell Biology and Anatomy X X X X 26 26 26.0499 Cell/Cellular Biology and Anatomical Sciences, Other. X X X X 26 26 26.0502 Microbiology, General X X X X 26 26 26.0503 Medical Microbiology and Bacteriology X X X X 26 26 26.0504 Virology X X X X 26 26 26.0505 Parasitology X X X X 26 26 26.0506 Mycology X 26 26 26.0507 Immunology X X X X 26 26 26.0508 Microbiology and Immunology X X X X 26 26 26.0599 Microbiological Sciences and Immunology, Other. X X X X 26 26 26.0701 Zoology/Animal Biology X X X X 26 26 26.0702 Entomology X X X X 26 26 26.0707 Animal Physiology X X X X 26 Animal Behavior and Ethology X X X X 26 26 26.0709 Wildlife Biology X X X X 26 26 26.0799 Zoology/Animal Biology, Other. X X X X 26 26 26.0801 Genetics, General X X X X 26 26 26.0802 Molecular Genetics X X X X 26 26 26.0803 Microbial and Eukaryotic Genetics X X X X 26 26 26.0804 Animal Genetics X X X X 26 26 26.0805 Plant Genetics X X X X 26 26 26.0806 Human/Medical Genetics X X X X 26 26 26.0807 Genome Sciences/Genomics X X X X 26 26 26.0899 Genetics, Other. X X X X 26 26 26.0901 Physiology, General X X X X 26 26 26.0902 Molecular Physiology X X X X 26 26 26.0903 Cell Physiology X X X X 26 26 26.0904 Endocrinology X X X X 26 26 26.0905 Reproductive Biology X X X X 26 26 26.0907 Cardiovascular Science X 26 26 26.0908 Exercise Physiology X
119 26 26 26.0909 Vision Science/Physiological Optics X 26 26 26.0910 Pathology/Experimental Pathology X X X X 26 26 26.0911 Oncology and Cancer Biology ? 26 26 26.0912 Aerospace Physiology and Medicine 26 26 26.0999 Physiology, Pathology, and Related Sciences, Other. X X X X 26 26 26.1001 Pharmacology X X X X 26 26 26.1002 Molecular Pharmacology X X X X 26 26 26.1003 Neuropharmacology X 26 26 26.1004 Toxicology X X X X 26 26 26.1005 Molecular Toxicology X 26 26 26.1006 Environmental Toxicology X X X X 26 26 26.1007 Pharmacology and Toxicology X 26 26 26.1099 Pharmacology and Toxicology, Other. X 26 26 26.1101 Biometry/Biometrics X 26 Biostatistics X 26 26 26.1103 Bioinformatics X 26 26 26.1104 Computational Biology X 26 26 26.1199 Biomathematics, Bioinformatics, and Computational Biology, Other. X 26 26 26.1201 Biotechnology X X X X 26 26 26.1301 Ecology X X X 26 26 26.1302 Marine Biology and Biological Oceanography X 26 26 26.1303 Evolutionary Biology X 26 26 26.1304 Aquatic Biology/Limnology X 26 26 26.1305 Environmental Biology X X X X 26 26 26.1306 Population Biology X X 26 26 26.1307 Conservation Biology X X X X 26 26 26.1308 Systematic Biology/Biological Systematics X 26 26 26.1309 Epidemiology X X X X 26 26 26.1310 Ecology and Evolutionary Biology X 26 26 26.1399 Ecology, Evolution, Systematics and Population Biology, Other. X 26 26 26.1401 Molecular Medicine X X X X 26 26 26.1501 Neuroscience X 26 26 26.1502 Neuroanatomy X 26 26 26.1503 Neurobiology and Anatomy X 26 26 26.1504 Neurobiology and Behavior X 26 26 26.1599 Neurobiology and Neurosciences, Other. X 26 26 26.9999 Biological and Biomedical Sciences, Other. X X X X 27 27 27.0101 Mathematics, General X X X X 27 27 27.0102 Algebra and Number Theory X X X X 27 27 27.0103 Analysis and Functional Analysis X X X X 27 27 27.0104 Geometry/Geometric Analysis X X X X 27 27 27.0105 Topology and Foundations X X X X 27 27 27.0199 Mathematics, Other. X X X X 27 27 27.0301 Applied Mathematics, General X X X X 27 27 27.0303 Computational Mathematics X X X X 27 27 27.0304 Computational and Applied Mathematics X X X X 27 Financial Mathematics X X X X 27 27 27.0306 Mathematical Biology X X X X 27 27 27.0399 Applied Mathematics, Other. X X X X 27 27 27.0501 Statistics, General X X X X 27 27 27.0502 Mathematical Statistics and Probability X X X X 27 27 27.0503 Mathematics and Statistics X X X X 27 27 27.0599 Statistics, Other. X X X X 27 27 27.9999 Mathematics and Statistics, Other. X X X X 28 28 28.0501 Air Science/Airpower Studies. X X X X 28 28 28.0502 Air and Space Operational Art and Science. 28 28 28.0505 Naval Science and Operational Studies. 29 29 29.0201 Intelligence, General 29 29 29.0202 Strategic Intelligence 29 29 29.0203 Signal/Geospatial Intelligence 29 29 29.0204 Command & Control (C3, C4I) Systems and Operations
120 29 29 29.0205 Information Operations/Joint Information Operations 29 29 29.0206 Information/Psychological Warfare and Military Media Relations 29 29 29.0207 Cyber/Electronic Operations and Warfare 29 29 29.0299 Intelligence, Command Control and Information Operations, Other. 29 29 29.0301 Combat Systems Engineering 29 29 29.0302 Directed Energy Systems X 29 29 29.0303 Engineering Acoustics 29 29 29.0304 Low Observables and Stealth Technology 29 29 29.0305 Space Systems Operations 29 29 29.0306 Operational Oceanography X 29 29 29.0307 Undersea Warfare 29 29 29.0399 Military Applied Sciences, Other. 29 29 29.0401 Aerospace Ground Equipment Technology 29 29 29.0402 Air and Space Operations Technology 29 29 29.0403 Aircraft Armament Systems Technology 29 29 29.0404 Explosive Ordinance/Bomb Disposal 29 29 29.0405 Joint Command/Task Force (C3, C4I) Systems 29 Military Information Systems Technology 29 29 29.0407 Missile and Space Systems Technology 29 29 29.0408 Munitions Systems/Ordinance Technology 29 29 29.0409 Radar Communications and Systems Technology 29 29 29.0499 Military Systems and Maintenance Technology, Other. 29 29 29.9999 Military Technologies and Applied Sciences, Other. 30 30 30.0101 Biological and Physical Sciences X X X X 30 30 30.0601 Systems Science and Theory X 30 30 30.0801 Mathematics and Computer Science 30 30 30.1001 Biopsychology X X 30 30 30.1701 Behavioral Sciences. X X X X 30 30 30.1801 Natural Sciences X X X X 30 30 30.1901 Nutrition Sciences X 30 30 30.2501 Cognitive Science X X 30 30 30.2701 Human Biology. X X 30 30 30.3001 Computational Science. X X X X 30 30 30.3101 Human Computer Interaction. 30 30 30.3201 Marine Sciences X 30 30 30.3301 Sustainability Studies. X 40 40 40.0101 Physical Sciences X X X X 40 40 40.0201 Astronomy 40 40 40.0202 Astrophysics 40 40 40.0203 Planetary Astronomy and Science 40 40 40.0299 Astronomy and Astrophysics, Other. 40 40 40.0401 Atmospheric Sciences and Meteorology, General X 40 40 40.0402 Atmospheric Chemistry and Climatology X 40 40 40.0403 Atmospheric Physics and Dynamics X 40 40 40.0404 Meteorology X 40 40 40.0499 Atmospheric Sciences and Meteorology, Other. X 40 40 40.0501 Chemistry, General X X X X 40 40 40.0502 Analytical Chemistry X 40 40 40.0503 Inorganic Chemistry X 40 Organic Chemistry X X 40 40 40.0506 Physical Chemistry X X 40 40 40.0507 Polymer Chemistry X 40 40 40.0508 Chemical Physics X X 40 40 40.0509 Environmental Chemistry X 40 40 40.0510 Forensic Chemistry X 40 40 40.0511 Theoretical Chemistry X 40 40 40.0599 Chemistry, Other. X X 40 40 40.0601 Geology/Earth Science, General X X X X 40 40 40.0602 Geochemistry X 40 40 40.0603 Geophysics and Seismology X 40 40 40.0604 Paleontology X
121 40 40 40.0605 Hydrology and Water Resources Science X 40 40 40.0606 Geochemistry and Petrology X 40 40 40.0607 Oceanography, Chemical and Physical X 40 40 40.0699 Geological and Earth Sciences/Geosciences, Other. X 40 40 40.0801 Physics, General X 40 40 40.0802 Atomic/Molecular Physics X 40 40 40.0804 Elementary Particle Physics X 40 40 40.0805 Plasma and High Temperature Physics 40 40 40.0806 Nuclear Physics X 40 40 40.0807 Optics/Optical Sciences X 40 40 40.0808 Condensed Matter and Materials Physics 40 40 40.0809 Acoustics 40 40 40.0810 Theoretical and Mathematical Physics X 40 40 40.0899 Physics, Other. X 40 40 40.1001 Materials Science X X X 40 40 40.1002 Materials Chemistry X 40 40 40.1099 Materials Sciences, Other. X X X 40 40 40.9999 Physical Sciences, Other. X X X X 41 41 41.0000 Science Technologies/Technicians, General X X X X 41 41 41.0101 Biology Technician/Biotechnology Laboratory Technician X X X X 41 Industrial Radiologic Technology/Technician 41 41 41.0205 Nuclear/Nuclear Power Technology/Technician X 41 41 41.0299 Nuclear and Industrial Radiologic Technologies/Technicians, Other. X 41 41 41.0301 Chemical Technology/Technician X X X X 41 41 41.0303 Chemical Process Technology X 41 41 41.0399 Physical Science Technologies/Technicians, Other. X X X X 41 41 41.9999 Science Technologies/Technicians, Other. X X X X 42 42 42.2701 Cognitive Psychology and Psycholinguistics 42 42 42.2702 Comparative Psychology 42 42 42.2703 Developmental and Child Psychology 42 42 42.2704 Experimental Psychology 42 42 42.2705 Personality Psychology X 42 42 42.2706 Physiological Psychology/Psychobiology 42 42 42.2707 Social Psychology X 42 42 42.2708 Psychometrics and Quantitative Psychology 42 42 42.2709 Psychopharmacology 42 42 42.2799 Research and Experimental Psychology, Other. 43 43 43.0106 Forensic Science and Technology X 43 43 43.0116 Cyber/Computer Forensics and Counterterrorism. 45 45 45.0301 Archeology. X 45 45 45.0603 Econometrics and Quantitative Economics. X X X 45 45 45.0702 Geographic Information Science and Cartography X X 49 49 49.0101 Aeronautics/Aviation/Aerospace Science and Technology, General. 51 51 51.1002 Cytotechnology/Cytotechnologist. X X X 51 51 51.1005 Clinical Laboratory Science/Medical Technology/Technologist. X X X 51 51 51.1401 Medical Scientist X X X 51 Pharmaceutics and Drug Design X X X 51 Medicinal and Pharmaceutical Chemistry X X X 51 Natural Products Chemistry and Pharmacognosy X 51 Clinical and Industrial Drug Development. X X X 51 Pharmacoeconomics/Pharmaceutical Economics. X 51 Industrial and Physical Pharmacy and Cosmetic Sciences. 51 Pharmaceutical Sciences. X X X 51 Environmental Health. X X X X 51 Health/Medical Physics. X X X 51 Veterinary Anatomy X X X X 51 Veterinary Physiology X X X X 51 Veterinary Microbiology and Immunobiology X X X X 51 Veterinary Pathology and Pathobiology X X X X 51 Veterinary Toxicology and Pharmacology X X X X 51 Veterinary Preventive Medicine Epidemiology and Public Health X X X X
122 51 Veterinary Infectious Diseases X X X X 51 Medical Informatics X X X X 52 Management Science X 52 Business Statistics X X 52 Actuarial Science X X 52 Management Science and Quantitative Methods, Other X TOTALS 424 196 179 177 324 PERCENT 46 40 42 76
123 APPENDIX C IRB APPROVAL
124 LIST OF REFERENCES Angrosino, M. V. (2007). Naturalistic observation Walnut Creek, CA: Left Coast Press. Association of Public and Land Grant Universities (2009). Human capacity development. Washington, DC: APLU. Appelbaum, P. (2008). Embracing mathematics: On becoming a teacher and changing with mathematics New York, NY: Routledge. Ary, D., Jacobs, L. C., Razavieh, A., & Sorensen, C. (2010). Introduction to research in education Belmont, CA: Cengage Learning. Aud, S., Hussar, W., Kena, G., Bianco, K., Frohlich, L., Kemp, J., & Tahan, K. (2011). The condition of education 2011. NCES 2011 033. National Center for Education Statistics. Balschweid, M. A. ( 2002). Teaching biology using agriculture as the context: Perceptions of high school students. Journal of Agricultural Education, 43 (2), 56 67. Balschweid, M. A., & Thompson, G. W. (2002). Integrating science in agricultural education: Attitudes of indian a agricultural science and business teachers. Journal of Agricultural Education, 43 (2), 1 10. Barrick, R. K. (1989). Agricultural education: Building upon our roots. Journal of Agricultural Education, 30 (4), 24 29. Becker, K., & Park, K. (2011). Effects of integrative approaches among science, technology, engineering, and mathematics (STEM) subjects on students' learning: A preliminary meta analysis. Journal of STEM Education: Innovations & Research, 12 (5), 23 27 Bidwell, C. E., & Kasarda, J. D. (1975). School district organization and student achievement. American Sociological Review, 55 70. Bitner, B. L. (1991). Formal operational reasoning modes: Predictors of critical thinking abilities and grades assigned by teachers in science and mathematics for s tudents in grades nine through twelve. Journal of Research in Science Teaching, 28 (3), 265 274. Boaler, J. (1993). The role of contexts in the mathematics classroom: Do they make For the Learning of Mathematics, 13 (2), 12 17. Boo ne, H., Gartin, S. A., Boone, D. A., & Hughes, J. E. (2006). Modernizing the agricultural education curriculum: An analysis of agricultural education teachers'attitudes, knowledge, and understanding of biotechnology. Journal of Agricultural Education, 47 (1 ), 78.
125 Brown, J. (2012). The current status of STEM education research. Journal of STEM Education: Innovations & Research, 13 (5) 7 11. Brunsell, E. (2012). Integrating engineering and science in your classroom Arlington, VA: NSTA Press. Burton, M., Daa ne, C., & Giesen, J. (2009). Infusing mathematics content into a methods course: Impacting content knowledge for teaching. Issues in the Undergraduate Mathematics Preparation of School Teachers, 1, 1 12. Caldas, S. J., & Bankston, C. (1997). Effect of scho ol population socioeconomic status on individual academic achievement. The Journal of Educational Research, 90 (5), 269 277. Camp, W. G., Broyles, T., & Skelton, N. (2002). A national study of the supply and demand for teachers of agricultural education in 1999 2001. Retrieved from: http://aaaeonline.org/files/supply_demand/teachersupply2002.pdf Cantrell, P., Pekcan, G., Itani, A., & Velasquez Bryant, N. (2006). The effects of engineering modules on student learning in middle school science classrooms. Journal of Engineering Education, 95 (4), 301 309. Chiasson, T. C., & Burnett, M. F. (2001). The influence of enrollment in agriscience courses on the science achievement of high school students. Journal of Agricultural Education, 42 (1), 60 70. Committee on Prospering in the Global Economy of the 21st Century. (2007). Rising above the gathering storm: Energizing and employing A merica for a brighter economic future. Washington, DC: National Academies Press. Connors, J. J. (1998). A regional delphi study of the perceptions of NVATA, NASAE, and AAAE members on critical issues facing secondary agricultural education programs. Journal of Agricultural Education, 39 37 47. Conroy, C. A ., Dailey, A. L., & Shelley Tolbert, C. (2000). The move to agriscience and its impact on teacher education in agriculture. Journal of Agricultural Education, 41 (4), 51 61. Conroy, C. A., & Walker, N. J. (2000). An examination of integration of academic a nd vocational subject matter in the aquaculture classroom. Journal of Agricultural Education, 41 (2), 54 64. Coolman, I. F. (199 2). Introduction. In C. W. Hall & W. C. Olsen (Eds.), The literature of agricultural engineering (3 10) Ithaca: Cornell Univers ity Press. Coppola, R. K., & Malyn Smith, J. (2006). Preparing for the perfect storm: A report on the forum taking action together Developing a national action plan to address the Reston, VA : International Technology Education Association
126 Corbin, J., & Strauss, A. (2008). Basics of qualitative research: Techniques and procedures for developing grounded theory Thousand Oaks, CA: Sage Publications Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural 2015 Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications. Darling Hammond, L. (1999). Teacher quality and student achievement: A review of state policy evidence Seattle WA: Center for the Study of Teaching and Policy, University of Washington. Darling Hammond, L., & Bransford, J. (2007). Preparing teachers for a changing world: What teachers should learn and be able to do San Francisco, CA: John Wiley & Sons. Denzin, N. K., & Lincoln, Y. S. (2013). The landscape of qualitative research Thousand Oaks, CA : Sage Publications. Dewey, J. (1933). How we think: A restatement of the relation of reflective thinking to the educational process Lexington, MA: Heath Dewey, J. (1998). Experience and education Indianapolis, IN: Kappa Delta Pi. Dimitri, C., Effland, A. B., & Conklin, N. C. (2005). The 20th century transformation of U S agriculture and farm policy. Retrieved from: http://www.researchgate.net/publication/46472927_The_20th_Century_Transfor mation_of_U.S._Agriculture_and_Farm_Policy/file/60b7d51e 844f9326d0.pdf Doolittle, P. E., & Camp, W. G. (1999). Constructivism: The career and technical education perspective. Retrieved from: http://scholar.lib.vt.edu/ejournals/JVTE/v16n1/ doolittle Dormody, T. J. (1993). Science credentialing and science credit in secondary school agricultural education. Journal of Agricultural Education, 34 (2), 63 70. Drache, H. M. (1996). History of U S agriculture and its relevance to today Prairie Vi llage, KS: I nterstate Publishers. Duncan, D. W., Ricketts, J. C., & Shultz, T. (2012). Science, math, social studies, and language arts achievement of high school students in complete programs of agriscience education. Online Journal for Workforce Educati on and Development, 5 (3), 4. Dun kin, M. J., & Biddle, B. J. (1974). The study of teaching New York NY : Holt, Rinehart and Winston.
127 Dyer, J. E., & Osborn e, E. W. (1996). Effects of teaching approach on achievement of agricultural education students with varying learning styles. Journal of Agricultural Education, 37 43 51. Dyer, J. E., & Osborne, E. W. (1999). The influence of science applications in agriculture courses on attitudes of illinois guidance counselors at model student teaching centers. Jour nal of Agricultural Education, 40 57 66. Edney, K. C. (2009). Evaluating the Mathematics Achievement Levels of Students Participating in the Texas FFA Agricultural Mechanics Career Development Event (Doctoral dissertation, Texas A&M University). Edwards M. C. (2004). Cognitive learning, student achievement, and instructional approach in secondary agricultural education: A review of literature with implications for future research. Journal of Vocational Education Research, 29 (3), 225 244. Ejiwa le, J. A. (2012). Facilitating teaching and learning across STEM f ields. Journal of STEM Education 13 (3) 87 94 Elliott, B., Oty, K., McArthur, J., & Clark, B. (2001). The effect of an interdisciplinary algebra/science course on students' problem solving skills, critical thinking skills and attitudes towards mathematics. International Journal of Mathematical Education in Science and Technology, 32 (6), 811 816. Enderlin, K., Petrea, R., & Osborne, E. (1993). Student and teacher attitude toward and performance in an integrated science/agriculture course. In Proceedings of the 47th Annual Central Region Research Confe rence in Agricultural Education. Fisher, D., & Frey, N. (2008). Better learning through structured teaching: A framework for the gradual release of res ponsibility Danvers, MA: ASCD. Fleischman, H. L., Hopstock, P. J., Pelczar, M. P., & Shelley, B. E. (2010). Highlights from PISA 2009: Performance of U S 15 year old students in reading, mathematics, and science literacy in an international context. NCE S 2011 004. National Center for Education Statistics Florida Department of Education (2013). 2013 2014 Engineering & Technology Education Curriculum Frameworks. Retrieved from: http://www.fldoe.org/workforce/dwdframe/eng_tech_frame13.asp Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., . West, P. C. (2011). Solutions for a cultivated planet. Nature, 478 (7369), 337 342. Fosnot, C. T. (1996). Constructivism: T heory, perspectives, and practice. New York, NY: Teachers College Press
128 Foutz, T., Navarro, M., Hill, R. B., Thompson, S. A., Miller, K., & Riddleberger, D. (2011). Using the discipline of agricultural engineering to integrate math and science. Journal of STEM Education: Innovations and Research, 12 (1), 24 32. Frick, M. J., Birkenholz, R. J., Gardner, H., & Machtmes, K. (1995). Rural and urban inner city high school student knowledge and perception of agriculture. Journal of A gricultural Education, 36 1 9. Fulton, K., & Britton, T. (2011). STEM teachers in professional learning communities: From good teachers to great teaching. Washington, DC: National Commission o n Gardner, D. P. (1983). A nat ion at risk. Washington, D.C.: The National Commission on Excellence in Education, U S Department o f Education. Garton, B. L., & Chung, N. (1996). The inservice needs of beginning teachers of agriculture as perceived by beginning teachers, teacher educato rs, and state supervisors. Journal of Agricultural Education, 37 52 58. Gasiewski, J. A., Eagan, M. K., Garcia, G. A., Hurtado, S., & Chang, M. J. (2012). From gatekeeping to engagement: A multicontextual, mixed method study of student academic engagemen t in introductory STEM courses. Research in Higher Education, 53 (2), 229 261. Glaser, B. G. (1964). The C onstant comparative met hod of qualitative analysis Social Problems 12 436 448 Goecker, A., Smith, P., Smith, E., & Goetz, R. (2010). Employment opportunities for college graduates in food, renewable energy and the environment: United states, 2010 2015. Goetz, L. (2012, The Agricultural Education Magazine 84, 5, 18 20. Gonzalez, H. B., & Kuenzi, J. J. (2012). Science, technology, engineering, and mathematics (STEM) education: A primer. Congressional Research Service, Library of Congress. Haug, K. (2011). CASE: Creating curiosity through agriculture. The Agricultural Education Magazine 84, 2, 7 8. Hayden, K., Ouyang, Y., Scinski, L., Olszewski, B., & Bielefeldt, T. (2011). Increasing student interest and attitudes in S TEM: Professional development and activities to engage and inspire learners. Contemporary Issues in Technology and Teacher Education, 11 (1), 47 69.
1 29 Haynes, J. C., Robinson, J. S., Edwards, M. C., & Key, J. P. (2012). Assessing the effect of using a science enhanced curriculum to improve agriculture students' science scores: A causal comparative study. Journal of Agricultural Education, 53 (2), 15 27. Hoffer, T. B. Rasinski, K.A., & Moore, W. (1995). Social background differences in high school mathematics and science coursetaking and achievement (NCES 95 206). Washington, DC: U.S. Department of Education Hofstein, A., & Rosenfeld, S. (1996). Bridging the gap between formal and informal science learning. Studies in Science Education, 28 (1), 87 112. Israel, G. D., Myers, B. E., Lamm, A. J., & Galindo Gonzalez, S. (2012). CTE students and science achievement: Does type of coursework and occupational cluster matter? Career and Technical Education Research, 37 (1), 3 20. Johnson, D. M., & Wardlow, G. W. (2004). Computer experiences, self efficacy, and knowledge of undergraduate students entering a land grant college of agriculture by year and gender. Journal of Agricultural Education, 45 (3), 53 64. Journal of STEM Education (2013). Archives Retrieved from http://ojs.jstem.org/index.php?journal=JSTEM&page=issue&op=archive Kantrovich, A. J. (2007). A national study of the supply and demand for teachers of agricultural education fro m 2004 2006. American Association for Agricultural Education. Retrieved from: http://naae.ca.uky.edu/links/resources/PDF/Mic rosoft%20Word%20 %202007%20Supply%20and%20Demand%20study%20report.pdf Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development Englewood Cliffs, NJ: Prentice Hall. Kuenzi, J. J. (2008). Science, technology, engineering, and mathematics (STEM) education: Background, federal policy, and legislative action. Congressional Research Service. Retrieved from: http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1034&context=crsdocs Lancelot, W. H. (1944). Permanent learning: A study in educational techniques J. Wiley & sons, inc. Lawrence, S. G., & Rayfield, J. (2012, School g ardens: Ripe with STEM and experiential learning. The Agricultural Education Magazine, 84 7. Layfield, K. D., Minor, V. C., & Waldvogel, J. A. (2001). Integrating science into r presented at the Proceedings of the Twenty Eighth Annual National Agricultural Education Research Conference,
130 Lee, J. S. (2000). Program planning guide for agriscience and technology education (2nd ed. ed.). Danville, Ill: Interstate Publishers. Lincol n, Y. S., & Guba, E. G. (1985). Establishing trustworthiness. Naturalistic Inquiry, 289 331. Martin, M. O., Mullis, I. V., Gonzalez, E. J., Gregory, K. D., Smith, T. A., Chrostowski, S. report. Study at the Eight Grade.Chestnut Hill, MA: Boston College, Marzano, R. J. (2007). The art and science of teaching: A comprehensive framework for effective instruction A scd. Matthews, M. R. (1994). Science teaching: The role of history and philosophy of science Psychology Press. Matthews, M. R. (1994). Science teaching: The role of history and philosophy of science Psychology Press. McGhee, M. B., & Cheek, J. G. (1990) Assessment of the preparation and career patterns of agricultural education graduates, 1975 1985. Journal of Agricultural Education, 31 (2), 17 22. McLean, R. C., & Camp, W. G. (2000). An examination of selected preservice agricultural teacher education programs in the united states. Journal of Agricultural Education, 41 (2), 25 35. Merriam, S. B. (2009). Qualitative research: A guide to design and implementation San Francisco, CA: J. Wiley & Sons. Miller, G., & Gliem, J. (1994). Agricultural education agriculturally related mathematics problems. Journal of Agricultural Education, 35 (4), 25 30. Miller, W. W., & Vogelzang, S. K. (1983). Importance of including mathematical concepts instruction as a part of the vocational agric ultural program of study. Ames, IA: Department of Agricultural Education, Iowa State University. M., . Smith, T. A. (2000). TIMSS 1999 international mathematics report. Myers, B. E., & Dyer, J. E. (2004). Agriculture teacher education programs: A synthesis of the literature. Journal of Agricultural Education, 45 (3), 44 52. Myers, B. E., & Washburn, S. G. (2008). Integrating science in the agriculture curriculum: Agriculture teacher perceptions of the opportunities, barriers, and impact on student enrollment. Journal of Agricultural Education, 49 (2), 27.
131 National Research Council (2009). Transforming agricultural education for a changing world. Washing ton, DC: The National Academies Press. National Research Council (2011). Successful K 12 STEM education identifying effective approaches in science, technology, engineering, and mathematics Washington, D C: The National Academies Press. National Science a nd Technology Council (2013). Federal STEM Education 5 Year Strategic Plan. Retrieved from: http://www.whitehouse.gov/sites/default/files/microsites/ostp/ stem_stratplan_201 3.pdf Newman, M. E., & Johnson D. M. (1993). Perceptions of M ississippi secondary agriculture teachers concerning pilot agriscience courses. Journal of Agricultural Education, 34 (3), 49 58. Oxford, R. L. (1997). Constructivism: Shape shifting, substance, and teacher education applications. Peabody Journal of Education, 72 (1), 35 66. Park, T. D., & Osborne, E. (2004). A model for the study of reading in school based agricultural education. In The Proceedings of Thirty First Annua l National Agricult ural Education Research Meeting. Parr, B. A., Edwards, M. C., & Leising, J. (2006). Effects of a math enhanced curriculum and instructional approach on the mathematics achievement of agricultural power and technology students: An experim ental study. Journal of Agricultural Education, 47 (3), 81 93 Perkins, D. (1999). The many faces of constructivism. Educational Leadership, 57 (3), 6 11. Phillips, D. C. (1995). The good, the bad, and the ugly: The many faces of constructivism. Educationa l Researcher, 24 (7), 5 12. Phipps, L. J., & Osborne, E. W. (1988). Handbook on agricultural education in pub lic schools Danville, IL: The Interstate Printers & P ublishers. Prawat, R. S. (1992). Teachers' beliefs about teaching and learning: A constructi vist perspective. American Journal of Education, 100 (3) 354 395. Rao, A. (1987). Food, agriculture and education. Oxford, UK: Pergamon Press. Ray, T. (2013). How I teach STEM in my agriculture classes. The Agricultural Education Magazine 85 ( 5 ) 26 27. Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. Journal of Educational Psychology, 104 (3), 700.
132 Richards, L. (2009). Handling qualitative data: A pr actical guide Thousand Oaks, CA: Sage Publications. Ricketts, J. C., Duncan, D. W., & Peake, J. B. (2006). Science achievement of high school students in complete programs of agriscience education. Journal of Agricultural Education, 47 (2), 48. Roberson, D. L., Flowers, J., & Moore, G. E. (2000). The status of integration of academic and a gricultural education in North C arolina. Journal of Career and Technical Education 17 (1), 39 53. Roberts, T. G., & Dyer, J. E. (2004). Characteristics of effective agr iculture teachers. Journal of Agricultural Education, 45 (1) 82 95. Sanders, M. (2009). Stem, stem education, stemmania. The Technology Teacher, 68 (4), 20 26. Seidman, I. (2012). Interviewing as qualitative research: A guide for researchers in education and the social sciences New York, NY: Teachers College P ress. Shinn, G. C., Briers, G. E., Christiansen, J. E., Harlin, J. F., Lindner, J. R., Murphy, T. H., . Lawver, D. E. (2003). Improving student achievement in mathematics: An important role for secondary agricultural education in the 21st century. Retrieved from: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=2F23B5220239C408D1 A13A08ACA7A270?doi=10.1.1.130.5 829&rep=rep1&type=pdf Shoulders, C. (2012). The effects of a socioscientific issues instructional model in secondary agricultural education on students content knowledge, scientific reasoning ability, argumentation skills, and views of the nature of scien ce (Doctoral dissertation, University of Florida ) Retrieved from http://ufdc.ufl.edu/UFE0044027 Smith, A. G., & Myers, B. E. (2012). Perceptions of florida secondary school principals toward agricultural education. Journal of Agricultural Education, 53 (3), 154 165. Stake, R. E. (1978). The case study method in social inquiry. Educational Researcher, 7 (2), 5 8. Stake, R. E. (2013). Multiple case study analysis New York, NY: Guilford Press. Stone III, J ., Alfeld, C., Pearson, D., Lewis, M., & Jensen, S. (2006). Building academic skills in context: Testing the value of enhanced math learning in career and technical education. St. Paul, MN: University of Minnesota, National Research Center for Career and T echnical Education.
133 Stone, J. R., Alfeld, C., & Pearson, D. (2008). Rigor and relevance: Enhancing high American Educational Research Journal, 45 (3), 767 795. Stripling, C. T., & Roberts, T. G. (2012a). Investigating the effects of a math enhanced agricultural teaching methods course. Journal of Agricultural Education, 54 (1), 124 138. Stripling, C. T., & Roberts, T. G. (2012 b ). Florida preservice agricultural education athematics ability and efficacy. Journal of Agricultural Education, 53 (1), 109 122. Stripling, C. T. (2012). Effects of mathematics integration on mathematical ability and efficacy of preservice teachers (Doctoral dissertation, University of Florida ) Re trieved from http://ufdc.ufl.edu/UFE0043943 Swortzel, K. A., Jackson, G. B., Taylor, W. N., & Deeds, J. P. (2003). Attitudes of mississippi secondary agricultural science and biology/business students toward information technology. Journal of Southern Agricultural Education Research, 53 (1), 286 299. Thompson, G. (1998). Implications of integrating science in secondary agricultural education programs. Journal of Agricultural Education, 39 (4), 76. Thoron, A. C ., & Myers, B. E. (2012). Effects of inquiry based agriscience instruction and subject matter based instruction on student argumentation skills. Journal of Agricultural Education, 53 (2), 58 69. Torres, R. M., Kitchel, T., & Ball, A. L. (2010). Preparing a nd advancing teachers in agricultural education. Columbus, OH: The Ohio State University. Trauger, A., Sachs, C., Barbercheck, M., Kiernan, N. E., Brasier, K., & Findeis, J. (2008). Agricultural education: Gender identity and knowledge exchange. Journal of Rural Studies, 24 (4), 432 439. Travers, M. (2001). Qualitative research through case studies Thousand Oaks, CA: Sage Publications. U.S. Immigration and Customs Enforcement (2013). STEM designated degree programs. Retrieved from: http://www.ice.gov/sevis/stemlist.htm Von Secker, C. E., & Lissitz, R. W. (1999). Estimating the impact of instructional practices on student achievement in science. Journal of Research in Science Teachin g, 36 (10), 1110 1126. Washburn, S. G., & Myers, B. E. (2010). Agriculture teacher perceptions of preparation to integrate science and their current use of inquiry based learning. Journal of Agricultural Education, 51 (1), 88.
134 Weinburgh, M. (1995). Gender differences in student attitudes toward science: A meta analysis of the literature from 1970 to 1991. Journal of Research in Science Teaching, 32 (4), 387 398. Yager, R. E. (1991). The constructivist learning model. Science Teacher, 58 (6), 52 57. Yin, R. K. (2003). Case study research: Design and methods Thousand Oaks, CA: S age Publications Young, R. B., Edwards, M. C., & Leising, J. (2008). Effects of a math enhanced A year long experimental study in agricultural power and technology. Journal of Southern Agricultural Education Research, 58 (1), 4 17.
135 BIOGRAPHICAL SKETCH his Father Gary Stubbs, Mother Cher Stillo, S tepfather Ed Stillo, Brother Aaron S tubbs, and S ister in law Christina Stubbs. Eric was born in Orlando, Florida. However, h is earliest memories are of growing up in Connecticut. In back to Florida with her sons to become a language arts tea cher. While attending the International Baccalaureate School, he was first exposed to philosophy and critical thinking through a theory of knowledge class. After beginning college at the Florida Institute of Technology as an electrical engineering major, h e decided teaching would be a more fulfilling career and transferred to the University of Florida to earn a mathematics degree with a minor in education. During his time at the University of Florida, Eric became interested and involved in agriculture throu gh the increasing popularity of locally grown foods and organics. Before beginning graduate school, he taught math for two years and directed an agricultural program for four years at a public high school Eric graduated with his tural education and communication in May, 2014, and moved on to pursuing his doctoral degree. Eric is an elite bike racer and intensive gardener. His other interests include sustainability, systems thinking, interdisciplinarity, and curriculum development.