AN EXPLORATION OF FACTORS THAT INFLUENCE

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AN EXPLORATION OF FACTORS THAT INFLUENCE STUDENT ENGAGEMENT IN SCIENCE

BY

VALERIE J. FORTNEY

A Dissertation Submitted to the School of Graduate Studies in Partial Fulfillment of the Requirement for the Degree of Doctor of Education

Southern Connecticut State University New Haven, Connecticut May 2016



   

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ABSTRACT Author:

Valerie J. Fortney

Title:

AN EXPLORATION OF FACTORS THAT INFLUENCE STUDENT ENGAGEMENT IN SCIENCE

Thesis Advisor:

Dr. Peter R. Madonia

Institution:

Southern Connecticut State University

Year:

2016 The purpose of this study is to explore the factors that influence student

engagement in science. Increases in student engagement positively correlate to improved student achievement. This study targeted the lack of clarity regarding the relationships between the complexity of instructional objectives, teacher self-efficacy, past achievement, student grade level, and student engagement. This correlational design method uses a quantitative approach that includes observations of student engagement levels and a student self-report survey of engagement, as indicators of student engagement levels. A multiple regression analysis of each measure of student engagement instruments determine the influence of each variable to student engagement. Influencing student engagement would be a valuable tool for educators in designing student intervention and improving student achievement. Keywords: Student Engagement, Science, Middle School, K-12, Instructional Objectives, Rigor, Teacher Self-Efficacy, Student Achievement, Gender, Grade

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This body of work is dedicated the two most important men in my life. To my husband, Matt Fortney: You make me laugh when I want to cry and you inspire me to get up again when I am knocked down. We did this together as a family. I love you to the moon and back. To my father, Robert Charles Hau: You raised me to have a sense of wonder about the world. Leading by example, you taught me that with hard work and determination I could do anything. I know you would have been so proud! I miss you so much.

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ACKNOWLEDGEMENTS

The doctoral degree and dissertation is a long and difficult journey that cannot be undertaken alone. I am indebted to many individuals for their support and guidance during this academic experience. I would like to thank my sponsor Dr. Peter Madonia, and my committee members, Dr. William Diffley and Dr. Robert Cronin. Your dedication of time and guidance made this work come to fruition. This doctoral degree would not have been the same without the support of my Cohort X “Girls:” Tiffany Hesser, Dawn Matera, and Amy Gagnon. This doctoral journey was an emotional test of overcoming obstacles. Our support system allowed us to vent, laugh and cry together. Most importantly, I learned I grew much stronger than I was at the beginning of this program. Special thank you to my friend and colleague, Patricia White, thank you for your relentless dedication to North Haven students. Your excitement about science teaching and learning is beyond compare. The learning process that comprises this research involved a lot of time trying to see through the mud. You inspired me to keep asking questions and not to fret when the answer is not within immediate reach. v

I am thankful for my colleagues at North Haven High School and North Haven Middle School. Without hesitation, they sprang into action to help with study development and data collection. You cheered me on, and supported me through the entire process. The overwhelming task of a simultaneous transition to a new school and completing a dissertation would not have been possible with your assistance. I will be forever grateful for your kindness. Additionally, I would like to thank my family for their constant encouragement. My in-laws, Bonnie and Frank Fortney, thank you for your year of support with the kids and invaluable hours of data collection. My children, Katelyn, Aaron and Eric Fortney, you understood that this was not going to be an easy journey. The deliveries of coffee, snacks and hugs to keep me motivated through all the long hours of hard work. Your love and encouragement renewed my energy to complete my degree. My husband Matt Fortney, who made sure I laughed, ate well, and exercised during this journey. I am eternally grateful for your love, encouragement, and support through these emotional years. Finally, I am forever grateful to my mother, Mary Ann Hau. You spent countless weeks collecting data, babysitting my children, playfully scolding me to get back to work, and being a shoulder to cry on when my world felt like it was falling apart. I love you so much.

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TABLE OF CONTENTS CHAPTER 1: INTRODUCTION ....................................................................................... 1 Problem Statement .................................................................................................. 2 Purpose Statement ................................................................................................... 4 Research Question .................................................................................................. 4 Hypotheses .............................................................................................................. 4 Study Significance .................................................................................................. 5 Theoretical Framework ........................................................................................... 7 Delimitations ......................................................................................................... 10 Limitations ............................................................................................................ 10 Definition of Terms............................................................................................... 11 Summary ............................................................................................................... 12 CHAPTER 2: LITERATURE REVIEW .......................................................................... 13 Student Engagement ............................................................................................. 13 Psychological Influences of Student Engagement ................................................ 15 Competency for Learning ..................................................................................... 17 Relationships ......................................................................................................... 24 Relevance of Learning .......................................................................................... 27 Teaching and Learning ......................................................................................... 28 Standards ............................................................................................................... 29 Curricula ............................................................................................................... 30 vii

Instructional Best Practices ................................................................................... 31 Increasing Student Engagement ............................................................................ 33 Improving Teacher Quality ................................................................................... 37 Summary ............................................................................................................... 43 CHAPTER 3: METHODOLOGY .................................................................................... 44 Introduction ........................................................................................................... 44 Research Question ................................................................................................ 44 Hypotheses ............................................................................................................ 44 Research Design.................................................................................................... 45 Target Population and Sample .............................................................................. 47 Sampling Procedures ............................................................................................ 48 Instrumentation ..................................................................................................... 50 Data Collection Procedures................................................................................... 56 Behavioral Observation of Students in Schools.................................................... 56 Science Activity Questionnaire............................................................................. 58 Teacher’s Sense of Efficacy Scale ........................................................................ 58 Instrument Linking Protocol ................................................................................. 59 Preliminary Analyses ............................................................................................ 59 Data Analysis ........................................................................................................ 60 Behavioral Engagement ........................................................................................ 60 Emotional and Cognitive Engagement ................................................................. 60 Preliminary Data Analysis .................................................................................... 60 Summary ............................................................................................................... 62 viii

CHAPTER 4: RESULTS .................................................................................................. 63 Introduction ........................................................................................................... 63 Purpose and Design............................................................................................... 63 Research Question ................................................................................................ 64 Hypotheses ............................................................................................................ 64 Participant Recruitment and Selection .................................................................. 65 Instrumentation and Data Collection .................................................................... 66 Research Question 1 ............................................................................................. 67 Multiple Regression Assumptions ........................................................................ 67 Multiple Regression Results ................................................................................. 73 Research Question 2 ............................................................................................. 76 Data Collection Procedures................................................................................... 76 Preliminary Analysis ............................................................................................. 77 Assumptions for Multiple Regression .................................................................. 78 Summary ............................................................................................................... 82 CHAPTER 5: DISCUSSION AND CONCLUSIONS ..................................................... 83 Statement of the Problem ...................................................................................... 83 Review of the Theoretical Framework ................................................................. 85 Summary of Research Methods ............................................................................ 86 Research Question 1 ............................................................................................. 86 Hypotheses 1 ......................................................................................................... 86 Research Question 2 ............................................................................................. 87 Hypotheses 2 ......................................................................................................... 87 ix

Discussion of Findings .......................................................................................... 87 Behavioral Student Engagement ........................................................................... 87 Emotional and Cognitive Student Engagement .................................................... 88 Implications........................................................................................................... 89 Recommendations for Practice ............................................................................. 90 Implications for Educational Policy...................................................................... 90 Implications for School Leaders ........................................................................... 91 Questions for Future Research .............................................................................. 93 Summary ............................................................................................................... 94 APPENDICES .................................................................................................................. 96 A. IRB Protocol and Consent Approval Letter .................................................... 97 B. Permission for Use (Figure 1) ......................................................................... 98 C. Permission for Use (Figure 2) ......................................................................... 99 D. Permission for Use (Figure 3) ....................................................................... 100 E. Permission for Use (Figure 4) ....................................................................... 101 F. Webb’s Depth of Knowledge (DOK) Levels ................................................ 102 G. Strategic School Profile for Quinnipiac Middle School ............................... 104 H. Informed Consent for Study Participation .................................................... 110 I. BOSS Observation Instrument ....................................................................... 116 J. SAQ Instrument Usage Agreement ................................................................ 117 K. SAQ Instrument ............................................................................................ 118 L. TSES Instrument Usage Agreement .............................................................. 124 M. TSES Instrument .......................................................................................... 125 x

N. Depth of Knowledge Flowchart .................................................................... 127 O. Hess’ Cognitive Rigor Matrix & Curricular Examples ................................ 129 REFERENCES ............................................................................................................... 130

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LIST OF TABLES Table 1. CMT Scores at QMS: Disaggregated Population Statistics............................... 49 Table 2. Instrument Sampling and School Population Sizes ........................................... 51 Table 3. Validity Measures for Behavioral Observation of Students in School .............. 53 Table 4. Reliability of Goal Scale Items for Science Activity Questionnaire ................. 54 Table 5. Reliability Measures for Teachers’ Sense of Efficacy Scale ............................. 55 Table 6. Reliability Statistics for the Science CMT ........................................................ 56 Table 7. Chi-Square Goodness of Fit Tests of Sample to Population (N=118) ............... 66 Table 8. TSES Scale Means and Standard Deviations (N=9) .......................................... 67 Table 9. Pearson Correlations of Engagement with Predictor Variables by Gender (N=118) ............................................................................................................ 69 Table 10. Regression Model Summary for Students Observed Engagement (N=118) ... 74 Table 11. ANOVA Table for Students Observed Engagement ....................................... 75 Table 12. Mean and Standard Deviation of Science Activity Questionnaire (N=118) ... 77 Table 13. Pearson Correlations of Self-Reported Cogitative and Emotional Engagement with Predictor Variables (N=118) .................................................................... 79

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LIST OF FIGURES Figure 1.

The Student Engagement Core Model ............................................................ 8

Figure 2. The Self-Determination Continuum ................................................................. 16 Figure 3. The Overall Self-Determination Model ........................................................... 17 Figure 4. The Path Analysis of Skill Performance .......................................................... 21 Figure 5. Alignment of research design instruments with theoretical framework ........... 46 Figure 6. Alignment of research design instruments with dimensions of student engagement ....................................................................................................... 51 Figure 7. Dual multiple regression of engagement measures .......................................... 61 Figure 8. Scatterplot of independent variables with male behavioral engagement observation measures ....................................................................................... 68 Figure 9. Normality of standardized residuals for passively engaged students ............... 70 Figure 10. Normality of standardized residuals for actively engaged students ................. 71 Figure 11. Scatterplots of linearity and homoscedasticity for female and male students ............................................................................................................. 72 Figure 12. Scatterplot of predictor variables with female and male student reported measures of engagement .................................................................................. 78 Figure 13. Normality of standardized residuals for female and male students’ self-reported engagement ....................................................................................................... 80 Figure 14. Scatterplots of linearity and homoscedasticity for female students self-reported engagement ....................................................................................................... 81

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CHAPTER 1 INTRODUCTION Since the implementation of No Child Left Behind, the nation has recognized the importance of measuring and improving student achievement. Maintaining our national economic and political status requires citizens to be on the cutting edge of science, technology and engineering (STEM). Trends in TIMMS and PISA scores indicate that U.S. students are not developing the critical thinking and problem solving skills they will need for the high tech STEM fields of the future. Major parts of the growing global economy include knowledge and technology intensive (KTI) industries. The U.S. has the highest KTI proportion of gross domestic product of any large economy (National Science Board, 2014). Supporting these growing industries requires a strong and engaging education system that promotes student achievement outcomes. High quality instruction that is rigorous and requires complex knowledge application promotes student achievement. Complex and rigorous activities are highly motivating and engaging and result in increased access of intended learning objectives by students. Teachers who deliver quality instruction that is student centered and engaging promote student achievement outcomes (Pasley, Weiss, Shimkus, & Smith, 2004). Educational reform and the reciprocal accountability process have been put into place to ensure that every child meets learning expectations. School leaders are required to determine the needs of students and teachers and provide the professional development support to improve student outcomes (Elmore, 2008). Ensuring that students learn has 1

led to massive changes in teacher evaluation and supervision. No longer does the act of teaching need to evaluated, but rather what should be evaluated is how those instructional practices improve student achievement (Aseltine, Faryniarz, & Rigazio-DiGilio, 2006). Problem Statement The central problem this study investigates is that students need to be critical thinkers and problem solvers to be successful in life. Darling-Hammond discussed in her book The Flat World and Education that entrepreneurs now have to compete for local business against global competitors. Technology has increased the speed of communication and dismantled barriers of the past. Geography has ceased to be the limiting factor for businesses to reach potential clients. The U.S. economy needs to have well educated citizens to be able support the economy at national and local levels. The rapid pace of technological innovation and changing skill demands has resulted in an estimated 85% of available jobs requiring specialized post-secondary training (Wagner, 2014). The Partnership for 21st Century Skills has called for greater emphasis on students’ civic, academic and interdisciplinary, digital, and global competencies and understandings (Partnership for 21st Century Skills, 2014). Student skill deficits have not been universal. The achievement gap between affluent communities and poor and minority communities has grown (Wagner, 2014). The skill deficits caused by educational inequality reinforces racial isolation and perpetuates poverty. Economic changes have drastically limited job opportunities for un-skilled employment. Increasing high school dropout rates and resulting high unemployment rates have been linked to crime and welfare dependency (Darling-Hammond, 2010). Monitoring student

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achievement and supporting schools to identify student-learning difficulties and develop interventions will help close the achievement gap (Darling-Hammond, 2010). Most recent data shows that U.S. students might not be receiving the essential academic skills they will need to compete globally in their careers (National Science Board, 2014). Currently, 18% of high school sophomore U.S. students do not meet basic proficiency levels in science. Comparing global academic achievement levels, U.S. students rank twenty-third in the world (Kelly, Nord, Jenkins, Chan, & Kastberg, 2013). U.S. fourth and eighth graders rank sixth and seventh respectively in the 2011 TIMMS achievement assessment (Provasnik et al., 2012). Students that are prepared for STEM careers will enable the U.S. to maintain its global competitiveness. Student achievement in science can be improved by focusing attention on critical thinking and problem solving skills. These skills will better prepare students for future STEM careers (National Research Council, 2000; National Research Council and the Institute of Medicine, 2003; National Science Board, 2014). The skills students need for the 21st century require authentic learning, mental model building, internal motivation, multiple intelligences and social learning (Trilling & Fadel, 2009). School leaders need to think beyond measures of high stakes testing to understand why some students begin to disengage with school. Progress monitoring engagement and motivation may be an additional way that educators can design intervention strategies to meet students’ individual learning needs. The development of intervention strategies to halt the disengagement process will improve students’ persistence in science through high school and into post-secondary careers (Archer et al., 2013; Archer & Tomei, 2014; DeWitt, Archer, & Osborne, 2014). 3

Identifying early indicators of disengagement and supporting the self-efficacy of students by providing relevant, rigorous, and student-centered instruction is imperative to improve student outcomes and increase student identification with science (Achieve Inc, 2015). Purpose Statement The purpose of this study is to understand the factors that influence student engagement in middle school science classrooms. Research Questions 1. What is the relationship between behavioral student engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? 2. What is the relationship between emotional and cognitive student engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? Hypotheses H01: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will not have a statistically significant relationship with behavioral student engagement. HR1: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will have a statistically significant relationship with behavioral student engagement. H02: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will not 4

have a statistically significant relationship with emotional and cognitive student engagement. HR2: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will have a statistically significant relationship with emotional and cognitive student engagement. Study Significance Currently, there are clear connections between student engagement and student achievement (Connell & Wellborn, 1991; Finn & Rock, 1997; Fredricks, Blumenfeld, & Paris, 2004; Lau, Roeser, & Kupermintz, 2002; McPartland, 1994; Sinclair & Christenson, 1998). However, it is unclear what factors, and to what extent, student engagement can be modified (Fredricks, Blumenfeld, & Paris, 2004). Students that are highly engaged are more likely to have higher levels of achievement (Ainley & Ainley, 2011; Dotterer & Lowe, 2011; Shui-fong et al., 2014). Students that have greater school engagement are less likely to drop out of school (Appleton, Christenson, & Furlong, 2008; Finn & Rock, 1997). The ability to diagnose disengagement and determine the underlying causes would be valuable in developing intervention plans for students and school improvement plans. Current research in the field of engagement does not address the dimensions of student engagement at the classroom level. Most student and teacher surveys measure school-wide engagement of students or overall engagement in a class. School-wide engagement measures the overall school connection but does not adequately discriminate the influencing factors of engagement needed to answer the research question and hypotheses. Course engagement does not allow for identification of student response to 5

instructional objective complexity level because of the changing nature of objectives over an instructional unit (Fredricks et al., 2011). Changing students’ perception of science before they make long-term decisions of self-efficacy are essential to promote long-term participation in science. Students decide if they like academic subjects between eleven and thirteen years of age. Students who dislike subjects, such as science, do not consider them for careers. The mechanism for the disengagement and non-identification trend is ambiguous. The science interest decline results in fewer students pursuing high-level science coursework (Archer et al., 2013; Archer & Tomei, 2014; DeWitt, Archer, & Osborne, 2014). The psychological factors of motivation contribute to student engagement and perseverance in scientific learning tasks. Understanding the psychological factors of motivation and how they influence student engagement are important for closing the achievement gap and improving academic outcomes for all. A lack of graduates pursuing STEM areas forces employers to seek foreign qualified candidates. Next Generation Science Standards (NGSS) focus on the 21st century skills students’ need and give relevance, coherence and depth to science instruction. Improving science content relevance and instructional strategies will give students the content knowledge, critical thinking, and problem solving skills they will need for future STEM careers (Achieve Inc, 2015). School leaders need to prioritize curricula and professional development for the results of NGSS to impact student outcomes. The diagnosis and identification of less optimal levels of student engagement can be used to identify professional development opportunities for teachers, intervention 6

programs for students, or curricular changes. These instructional and school-wide interventions may prevent student disengagement and improve academic outcomes for students (Wang & Eccles, 2013). Theoretical Framework Student engagement is defined as the degree of attention, curiosity, interest, optimism, and passion that students show when they are learning (Great Schools Partnership, 2014). Motivation of students to learn and progress in their education is an extension of student engagement. Students’ commitment to, valuing of, and connection with the people, educational goals, and outcomes promoted by a school enhances their motivation to learn and persist in difficult tasks (Finn, 1989). This research study uses the Student Engagement Core (SEC) Model (Figure 1) as a framework for understanding the interactions between teacher efficacy, instructional objective complexity, student achievement, and student grade level on science student engagement.

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Figure 1. The Student Engagement Core Model. Note. Adapted from “Where student, teacher, and content meet: Student engagement in the secondary school classroom,” by Corso, M. J., Bundick, M. J., Quaglia, R. J., & Haywood, D. E., 2013. American Secondary Education, 41(3), p. 55. Copyright 2013 by the American Secondary Education. Reprinted with Permission, see Appendix B. The heart of the PELP framework comprises of the instructional core (Childress, Elmore, Grossman, & Johnson, 2007). The instructional core examined the relationships between the student, teacher and content (City, Elmore, Fiarman, & Teitel, 2009). The Student Engagement Core (SEC) model expands the instructional core by integrating the psychological factors that influence student engagement. According to Corso, Bundick, Quaglia, and Haywood (2013), the following drive student engagement: relationships with teachers and peers, relevance of the content to student needs and interests and their self-efficacy and confidence in their ability to utilize the curricular content, teachers’ content and pedagogical expertise to deliver quality, rigorous instruction utilizing instructional best practices. 8

Both the combined components of the instructional core and the psychological factors of learning motivation lead to a better understanding of the decision-making process that lead to disengagement of students and undesirable academic outcomes. This model addresses the need to understand how classroom level engagement influences the degree of connectedness students feel to school. Increasing the sense of belongings at the classroom level will positively influence academic outcomes (Corso, Bundick, Quaglia, & Haywood, 2013). The student, his or her personality traits and prior experiences contribute to their feelings of connectedness to the classroom environment. The social interactions and structure of the teacher, other adults and peers determine the degree of feeling of support, emotional connections, and respect. Positive school-based relationships contribute to the students’ level of self-efficacy and their perception of their abilities as compared to others within the classroom structure. Finally, academic content contributes to the sense of value in learning and drives motivation to be persistent in difficult tasks. Students that feel competent in tasks in tern develop confidence, which ignites their interest in learning. Increased interest stimulates engagement, which positively influences student achievement and academic outcomes (Corso, Bundick, Quaglia, & Haywood, 2013). Relevance of content to the student builds engagement by connecting the content to the students’ current and future goals. Connecting content to goals builds the students sense of identity of self as a learner within the content area. Expertise of the teacher with the content allows innovative and effective pedagogical strategies to engage students. Quality instruction, caring environments, high expectations, real world examples and peer-learning are all highly engaging activities. These highly engaging activities require 9

facilitation by teachers with considerable expertise and skill to craft engaging units, scaffold learning and develop student self-efficacy though positive relationships and mutual respect (Corso, Bundick, Quaglia, & Haywood, 2013). Delimitations This study did not explore all areas that influence engagement. The scope of this study does not include the science teachers’ effectiveness, years of teacher experience, professional development opportunities, teacher training, or the appropriateness of curriculum and instruction. The study does not analyze the methods of instruction, only the instructional level as defined by Webb’s Depth of Knowledge (DOK). This study is limited to middle school science students in grades six through eight. The study did not consider the students’ prior history of negative behavior, full academic history, ethnicity, socio-economic status (including free or reduced lunch status), nor did the study differentiate between students who were in need of special education services or 504 accommodations and/or modifications. Additionally, the level of engagement measured does not include measures of student learning attributed to the level of engagement. Limitations Providing parents with the opportunity to have informed consent to participate in the study was a top priority. Parental consent and student assent signatures were required for study participation. Participants were selected from the list of assenting students whose parents gave consent. Students and parents received reminders to return consent forms if they did not do so initially. The intention was to reduce the possibility that the resulting sample may not accurately represent the student population. To prevent this 10

from affecting the outcome of the study, the sample was compared to the population using CMT scores and student demographic criteria. Student surveys address the need to consider non-observable measures of student engagement. These measurements were limited to the students’ comfort level in reporting these dimensions. To address these potential concerns, the protocol used educators from the district other than the teacher to proctor the survey. Definition of Terms Demographic Reference Groups (DRGs). A classification system in which districts that have public school students with similar socioeconomic status (SES) and need are grouped together. Grouping like districts together is useful in order to make legitimate comparisons among districts (Prowda, 2006). Student Engagement. Student engagement is the degree of attention, curiosity, interest, optimism, and passion that students show when they are learning or being taught. This extended to the level of motivation they have to learn and progress in their education (Great Schools Partnership, 2014). Instructional Objectives. An instructional objective is a description of a performance as the intended result of instruction. Objectives should be specific, outcome based, measurable, and describe the learner's behavior after instruction (Mager, 1997). Professional Learning Communities (PLCs). Groups of educators that are designed to utilize data to impact the school improvement process. PLCs are designed to build capacity among teachers to embed professional development and school improvement process into the school and district culture (Dufour & Eaker, 1998).

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Student Achievement. The degree that students are able to demonstrate learning on standardized learning assessment tools. These tools are designed to measure student performance as a result of delivered instruction (Cunningham, 2012). Socioeconomic Status (SES). The social standing or class of an individual or group. The measurement is often a combination of education, income and occupation (American Psychological Association, 2010). Summary Student achievement and halting the disengagement process is a national priority to maintain the U.S.’s status as a world leader. The process of disengagement is a gradual process that may be intervened by promoting school-based relationships, providing relevant and rigorous curricula and supporting teachers in developing their pedagogical skills. Monitoring student engagement and supporting students in their development of self-efficacy as learners has great potential to impact the development of school wide improvement plans. Improving student engagement before academic failure has occurred will lead to greater persistence and determination of learning. Positive attitudes toward school and learning lead to improved academic outcomes for students.

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CHAPTER 2 LITERATURE REVIEW Student Engagement Student engagement is defined as the degree of attention, curiosity, interest, optimism, and passion that students show when they are learning or being taught (Great Schools Partnership, 2014). Student engagement is more than simply appropriate classroom behavior. Students that are obedient yet only passively engaged are also limited in their learning capacity. Student engagement is described as a meta-construct of behavioral, cognitive and emotional factors (Appleton, Christenson, & Furlong, 2008; Fredricks, Blumenfeld, & Paris, 2004; Wang, Willett, & Eccles, 2011). High levels of student engagement result in increased student motivation and persistence in achieving learning expectations. Research on the malleability of student engagement shows that both psychological and contextual factors influence students’ sense of belonging in school and result in increased persistence in learning (Appleton, Christenson, Kim, & Reschly, 2006; Connell, 1990; Finn & Rock, 1997; Jimerson, Campos, & Greif, 2003). This study intends to clarify the relationships between indicators and influences on student engagement (Skinner, Furrer, Marchand, & Kindermann, 2008). Understanding the malleability of student engagement may assist educators in preventing student disengagement and academic failure.

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Approximately 40% to 60% of high school students are chronically disengaged. In sixth grade, 30% of students report being bored with school, but by tenth grade, it climbs to 49% (QISA Aspirations Research Center, 2014). The consequences to chronic student disengagement are varied. Students who were disengaged from school often learned fewer skills and content knowledge. Students that managed to graduate accepted second choice schools and career opportunities. Students from economically disadvantaged backgrounds were far less likely to graduate and faced lifelong struggles of increased unemployment and poverty (Connell, 1990; Connell, Spencer, & Aber, 1994; Connell & Wellborn, 1991; Marks, 2000; Skinner, Kindermann, & Furrer, 2009). Students at risk for school failure include those that are not motivated to learn and those that are actively and passively disengaged students (Bundick, Quaglia, Corso, & Haywood, 2014; Chapman, Laird, Ifill, & KewalRamani, 2011; Faria, Freire, Galvao, Reis, & Baptista, 2012; Finn, 1989; Finn & Rock, 1997; Fredricks et al., 2011; McPartland, 1994). While schools do not control all contributing factors to student engagement, providing high standards and quality instruction while supporting students in their educational and career goals fosters student engagement. This relationship has focused research to determine ways to promote student engagement in science for at-risk student populations (Faria, Freire, Galvao, Reis, & Baptista, 2012; Thompson & Windschitl, 2002). Strong connections between student engagement and academic achievement have been the focus of research for school improvement (Appleton, Christenson, & Furlong, 2008; Connell, Spencer, & Aber, 1994; Connell & Wellborn, 1991; Finn & Rock, 1997; National Research Council and the Institute of Medicine, 2003; Skinner, Wellborn, & 14

Connell, 1990). How students perceive their school context in meeting their psychological needs influences student engagement (Connell, 1990; Deci & Ryan, 2000; Krapp, Hidi, & Renninger, 1992). Students who do not have these psychological needs met have lower academic motivation to learn (Connell & Wellborn, 1991; Deci & Ryan, 2000; Krapp, 2005). Psychological Influences of Student Engagement Student engagement is described as an amalgamation of the student’s motivation to learn (Brophy, 2014), learning goals (Ames, 1992) and intrinsic motivation (Harter, 1982). A student’s interest level is a powerful influence on their learning. Student interest influences their level of attention, goal development and levels of learning. The development of personal meaning and relevance is an important factor in students’ enjoying science and focusing their attention to expand their knowledge and understanding. Students’ interest level is a combination of their personal value of science, enjoyment of science, interest in learning science and their continued interest in learning (Ainley & Ainley, 2011). Effective instruction means captivating student attention and resulting in students that desire to learn (Willis, 2006). Highly engaging instruction places the student as the decision maker and creates renewed focus on instructional objectives. Student interests can be learned through the development of student-teacher relationships. This process often starts with the asking of authentic questions to learn the students’ life experiences, skills, interests and goals (Blumenfeld et al., 1991).

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Behavior Type of Motivation

Non self-determined

Amotivation

Self-determined

Extrinsic Motivation

Intrinsic Motivation

Type of Regulation

NonExternal Introjected Identified Integrated regulation Regulation Regulation Regulation Regulation

Locus of Causality

Impersonal

External

Somewhat Somewhat External Internal

Internal

Intrinsic Regulation

Internal

Figure 2. The Self-Determination Continuum, showing the motivational, self-regulatory, and locus of causality factors influence of behaviors that vary by the level of one’s selfdetermination. Note. Adapted from “The 'what' and 'why' of goal pursuits: Human needs and the self-determination of behavior” by E. Deci, and R. Ryan, 2000, Psychological Inquiry, 11(4), p. 237. Copyright 2000 by Taylor & Francis Group, LLC. Reprinted with Permission, see Appendix C. Self-determination theory (SDT) describes the psychological needs of intrinsic motivation. The source and type of regulation influences an individual’s level of determination and persistence (Figure 2). Unmotivated individuals, according to SDT theory, often lack either a sense of self-efficacy or a sense of control of a situational outcome (Deci & Ryan, 2000). Student Engagement Core model supports the notion that instruction will improve student engagement through the development of self-efficacy and persistence and result in increased skill performance. Skill development, through the support of these influencing factors, may build learning relevance, which would result from students’ consideration of science-based careers. Skill development and competency builds upon an individual’s psychological investment for learning. This investment is demonstrated by students’ preference for and persistence in tackling

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learning challenges that go beyond their learning requirements (Connell & Wellborn, 1991). The overall self-determination model of motivational development (Figure 3) describes the effects of learning context on the development of the student’s self-identity. How students’ define themselves and their coping strategies of the social and learning stress impact their learning and achievement outcomes. Students who struggle to identify themselves as able to learn are more likely to engage in coping strategies that often lead to reduced learning opportunities and perpetuate the disengagement process (Skinner & Chi, 2012).

Figure 3. The Overall Self-Determination Model of Motivational Development. Note. Adapted from “Intrinsic Motivation and Engagement as “Active Ingredients” in GardenBased Education: Examining Models and Measures Derived From Self-Determination Theory” by E.A. Skinner, U. Chia, and The Learning-Gardens Educational Assessment Group, 2012, The Journal of Environmental Education, 43(1), p. 18. Copyright 2012 by the Taylor & Francis Group, LLC. Reprinted with Permission, see Appendix D.

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Competency for Learning Supporting student psychological needs may be a way educators can improve student engagement. The withdrawal cycle began with non-participation, which produced unsuccessful school outcomes and emotional withdrawal. Students that do not identify with school used physical withdrawal coping strategies often observed as nonparticipation. Development of high emotional connections with school enhances participation in school activities (Finn, 1989). The research is unclear whether this is an indicator or an influencer of engagement. Unfortunately, research has consistently shown that student interest in learning declines rapidly in early adolescence (Archer et al., 2010; Marks, 2000; Rose & Akos, 2014; Sorge, 2007; Vedder-Weiss & Fortus, 2011; Wang & Eccles, 2012; Yamamoto, Thomas, & Karns, 1969). Developing competency by providing support as they learn helps students complete challenging instructional tasks (Blackburn & Williamson, 2009). Learning ability and style preferences are a function of a student’s genetic predisposition and life experiences. Matching instructional strategies to student learning preferences results in increased competency, engagement and perseverance for learning (Gollub & National Research, 2002). Experiences during students’ elementary school years have been found to have a formative and lasting influence on students’ science beliefs, attitudes, and future career choices (Blatchford, 1992; Jarvis & Pell, 2005; Musgrove & Batcock, 1969). Early education experiences shape students’ perceptions of self-efficacy and self-identity (Lindahl, 2007). The decline in students’ identification with science through the middle school years are far greater than in other subject areas. As a result, many students do not consider coursework beyond the compulsory level (Archer et al., 2010, 2012; Archer et 18

al., 2013; Archer & Tomei, 2014; Osborne, Simon, & Collins, 2003; Yamamoto, Thomas, & Karns, 1969). The combination of a student’s successes and failures in school shape their identity: who they are and what they can become (Bandura, 1997). The decline in students pursuing science education beyond required courses has resulted in fewer students entering STEM fields. Changes in technology and the global economy have resulted in a surge of science and STEM career opportunities. Businesses report that higher unemployment rates have not made filling job vacancies any easier because available workers do not have the specialized skills these jobs require (Confederation of British Industry & Education Development International, 2011; Langdon, McKittrick, Beede, Khan, & Doms, 2011; National Science Board, 2014). Encouraging individuals and groups that do not identify with science, such as woman and minorities, may increase their persistence in learning and result in decreased unemployment (Blatchford, 1992; Lent, Brown, & Larkin, 1986; Post-Kammer & Smith, 1985, 1986). Engaging students in scientific practices and language proficiency may change the students’ identities for STEM careers and advanced science courses. The development of self-identity is influenced by the students’ prior success in science and how the students’ families value science (Roushias, Calabrese Barton, & Drake, 2009; Rozek, Hyde, Svoboda, Hulleman, & Harackiewicz, 2014). Family-based programs and museums have been developed with the intent to break down gender stereotyping and expand career exploration (McCubbins, Thomas, & Vetere, 2014). Closing gender and race wage gaps though the expansion of self-efficacy during science learning promotes science learning persistence; this will increase the potential for the consideration of science-based careers. 19

The autonomy of learning promotes student engagement by transferring learning decision making to the student (Mosston & Ashworth, 1985, 1990). Control of one’s learning is a key component to promoting student engagement. Fostering student identification with the skill being learned and providing learning autonomy grants students the ability to learn content deeply based on their individual interests (Bachelor, Vaughan, & Wall, 2012; Marzano, 2007; Stipek, 2002; Willis, 2006). The development of student self-efficacy can be achieved through the gradual release of learning control by the teacher to the student (Mosston & Ashworth, 1985, 1990). Student-teacher shared decision making is an important support component of this process. As the student achieves mastery of learning objectives, there is a gradual reciprocal exchange of control. The growing control over learning allows the learner to deepen skills while preventing the student from being overwhelmed as a result of frustration with a difficult task. The autonomy of learning promoted student engagement by transferring learning decision making to the student (Barber & Buehl, 2012; Faria, Freire, Galvao, Reis, & Baptista, 2012; Mosston & Ashworth, 1985, 1990; Ryan & Deci, 2000; Shernoff, Csikszentmihalyi, Shneider, & Shernoff, 2003; Skinner & Chi, 2012; Watters & Diezmann, 2013). The cause of the drop in engagement, motivation and identification in science may be due to social, developmental or instructional changes that also occur in the middle school years. School culture (Vedder-Weiss & Fortus, 2011, 2012), instructional strategies (Meece, Herman, & McCombs, 2003), peer influences (DeWitt, Archer, & Osborne, 2013; Simpson & Oliver, 1990) and home environments (Vedder-Weiss & Fortus, 2011) have been linked to declines in engagement, motivation and identification 20

in science. Wang and Eccles (2013) found that school characteristics such as support structures and positive teacher/peer relationships were the most influential in increasing behavioral engagement in middle school students. Additionally, they found that past academic achievement scores co-varied with the effect of learning choice on the level of behavioral engagement. Applying these findings to SDT, suggest that lower achieving students may not have a needed level of self-efficacy to benefit from choices in learning. Instruction with appropriate scaffolded support for students can be effective in promoting student persistence in learning (Deci & Ryan, 2000; Skinner & Belmont, 1993; Wood, Bruner, & Ross, 1976). Suggested methods of instructional scaffolding include structuring and problematizing. Support structures have been shown to reduce the complexity and narrow choices to support struggling learners achieve greater mastery of learning objectives. When scaffolded support is used with open-ended or complex tasks, such as the automation of non-salient tasks, it can promote articulation and reflection of

Figure 4. The Path Analysis of Skill Performance. Note. Adapted from “Self-efficacy perspective on achievement behavior” by D.H. Schunk, 1984, Educational Psychologist, 19(1), p. 51. Copyright 1984 by the American Psychological Association. Reprinted with Permission, see Appendix E. 21

learning objectives. Properly scaffolded learning tasks promotes student self-efficacy and prevents disengagement. Problematizing focuses students’ attention on issues, engages students, and promotes interest in aspects of the problem (Quintana et al., 2004; Reiser, 2004). A student’s identity develops over time. Researchers have observed consistent drops in identification with instruction and confidence in learning that coincides with the middle school years (Eccles, Midgley, & Adler, 1984; Harter, 1982; Simmons, 1987; Yamamoto, Thomas, & Karns, 1969; Zimmerman, 1997). Schunk’s path analysis model (Figure 4) describes the relationship between students’ skill development and the influences of instruction, self-efficacy and persistence for learning (Schunk, 1984). The interactions between the path analysis model of skill performance and the Corso, Bundick, Quaglia, and Haywood (2013) model of student engagement further the need to understand how situational context, psychological needs and individual characteristics and past experiences influence student engagement. Low levels of student ability to identify with science results in lower persistence of learning, lower self-efficacy and reduced skill development (Archer et al., 2010; Archer & Tomei, 2014; Johnston & Roberts, 2011; Weber, 2012). Students with high efficacy beliefs are reinforced when they successfully engage in challenging learning activities. Self-efficacy is an individual’s belief in his or her capability to perform a task. These beliefs influence the level of effort, persistence, and choice of activities (Bandura, 1977). Increased self-efficacy perceptions positively affect academic performance by increasing the amount of effort used, sustained persistence and the difficulty level of chosen activities (Hackett & Betz, 1997; Lent, Brown, & Larkin, 1986). Self-efficacy of 22

learning is a strong predictor of the range of potential occupations a student considers (Hackett & Betz, 1997). It is unclear what causes the reduction in student self-efficacy in science from grades five through nine. Ethnographic studies of fourth and sixth grade found that when students’ social identity work paralleled rigorous science learning, their ability to identify with science increased. Additionally, they found that academic success in school science correlated with liking science and increased science engagement. Factors of race, class, and gender impact students’ self-identity, motivation and persistence to learn science (Carlone, Scott, & Lowder, 2014). Studies of low socio-economic African-American middle school science students shows science instruction is often less rigorous than other subjects, resulting in academic success in science as nothing more than a measure of “good behavior” (Varelas, Kane, & Wylie, 2011). Examining differences in perceptions of self-efficacy in eighth and ninth grade students show differences when comparing the consideration of careers that were traditionally either male or female (Post-Kammer & Smith, 1985). In contrast, student interest in science in ten-year-olds showed no significant differences by gender (Murphy & Beggs, 2005). When math and science interest and efficacy was examined across socioeconomic status and gender, self-efficacy was reduced in both female and economically disadvantaged student groups (Post-Kammer & Smith, 1986). While students report enjoying science in elementary and middle school grades, they do not identify with and do not consider science-based careers (Archer et al., 2010, 2012; Archer et al., 2013; Archer & Tomei, 2014). The resulting non-identification with science may

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begin with students’ report that school science is vastly different from a science career (Archer et al., 2010). Promoting science self-efficacy through scaffolding increases the range of potential career paths a student may consider (Hackett & Betz, 1997). Expanding science student self-identities by breaking down the cultural norms of gender, race and social class will help increase persistence in the learning of science education (Eccles, 1994; Riegle-Crumb, Moore, & Ramos-Wada, 2011). Relationships Positive relationships with teachers promote student engagement (Anderson, Christenson, Sinclair, & Lehr, 2004; Fredricks, Blumenfeld, & Paris, 2004; Furrer & Skinner, 2003; Klem & Connell, 2004). Increased student engagement benefits both the student and teacher; potential influencing factors include school policy, teachers and parents (Zyngier, 2008). Teaching is ranked as one of the most stressful occupations based upon reports of physical and psychological well-being and low levels of job satisfaction (Johnson et al., 2005). Based upon the principles of Berne’s (1961) Transactional Model of psychotherapy, Lazarus (1966) developed the Transactional Model of Stress and Coping. This model describes an individual’s reaction to stress as guided by his or her interpretation of a stressor, which in turn triggers an emotional response. Emotions play a key role in the response to stress and the described “burnout process” (Lazarus, 2006; Montgomery & Rupp, 2005). Teachers report that strong and rewarding classroom relationships are a source of motivation and positive emotions (Hargreaves, 2000). Positive adult-student relationships in school are associated with

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higher academic achievement and reduced disciplinary problems (Crosnoe, Kirkpatrick Johnson, & Elder Jr, 2004). The development of teacher and peer relationships fosters a supportive environment, which is essential to a student’s sense of independence and belonging. (Connell, Spencer, & Aber, 1994; Skinner, Wellborn, & Connell, 1990). Supportive environments create a safe and trusting atmosphere that enables students to engage in intellectual risk taking (Beghetto, 2009). Intellectual risk taking is the adaptive behavior, such as sharing ideas or asking questions, that promotes the learning process (Byrnes, Miller, & Schafer, 1999; Clifford, 1991). Engagement in learning reduces a student’s likelihood of engaging in risky behaviors that may lead to withdraw from school (Archambault, Janosz, Fallu, & Pagani, 2009; Archambault, Janosz, Morizot, & Pagani, 2009; Connell, Spencer, & Aber, 1994; Voelkl, 1997). Student's self-efficacy for learning and perceived relationships with peers and teachers, the emotional and behavioral engagement of the student, and family science valuation all effected students’ learning motivation. High levels of relationship engagement promoted a better environment for student learning (Connell, Spencer, & Aber, 1994; Connell & Wellborn, 1991). Teachers’ motives for being a teacher, pedagogical skills, self-efficacy and school-based relationships influence their perceptions of the level of student engagement in their classes (van Uden, Ritzen, & Pieters, 2013). Teachers that promoted autonomy in a structured environment foster student motivation. High levels of behavioral engagement correlate positively to classroom structure and consistent expectations.

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Emotional engagement levels were greater when students felt that teachers were highly involved. Positive rapport with their teacher made students feel happier and more enthusiastic about their learning experiences (Skinner & Belmont, 1993). Children who had positive experiences with teachers develop optimal control profiles that promote active engagement. Students who experience unsupportive teachers have greater classroom disaffection and lower achievement. A rapid decline in student performance results when increased classroom disaffection and lower achievement creates a feedback loop. This decline damages student-teacher relationships and decreases both student engagement and motivation to learn and teacher self-efficacy (Skinner, ZimmerGembeck, & Connell, 1998). Intrinsic motivation development involves three basic psychological needs: the degrees of competence, autonomy and relatedness of content (Connell, Spencer, & Aber, 1994; Connell & Wellborn, 1991; Skinner & Chi, 2012). The processes of student motivation development are very similar to the motivation of teachers. Promoting student-teacher relationships and the cooperative learning processes will improve both instructional quality and student achievement. The classroom context is a significant factor in influencing student engagement, instructional quality, socio-emotional climate of the classroom, and student–teacher relationships (Dotterer & Lowe, 2011). Smaller schools may more easily foster studentteacher relationships. Smaller schools reported lower levels of absenteeism, greater participation levels, and students reported a general warmer environment (Finn & Voelkl, 1993). Restructured schools with several reform practices had consistently increased student achievement and engagement levels. Smaller restructured schools produced more significant and equitable achievement and engagement gains (Lee & Smith, 1995). 26

Students’ lack of motivation to learn science may be due to the way science is taught in schools and that ‘‘in some senses, school science education might do more harm than good!’’(Osborne, Simon, & Collins, 2003, p. 1060). Changes in the classroom environment and instructional practices towards less dynamic and student-centered modalities have been linked to declines in student attitude and motivation to learn (Logan & Skamp, 2008; Piburn & Baker, 1993; Simpson & Oliver, 1990; Speering & Rennie, 1996). Relevance of Learning The degree of influence between the interactions of the individual and the learning context may have been an influencing factor that stimulates a student’s level of engagement; therefore, student engagement directly influences student achievement (Connell, 1990; Finn & Rock, 1997; Fredricks, Blumenfeld, & Paris, 2004). Student engagement is dependent on the capacity of teachers to create high interest by providing a rich learning environment (Watters & Diezmann, 2013). The influences of content standards and inquiry standards are an essential part of learning performance construction (Krajcik, McNeill, & Reiser, 2008). These learner performances create high interest learner experiences to stimulate student engagement and an excitement for learning. Finn (1989) described the participation-identification model as the interaction of quality instruction with student participation in school activities and successful performance outcomes (Finn, 1989). Learning that was authentic, connected to real world experiences, and driven by content-based discourse promoted a higher order-learning environment. Challenging and level-appropriate work that is important to the student promotes a deep understanding of 27

and develops problem solving skills. Conceptual work was found to be more motivating than traditional direct instruction (Stipek, 2002). Improving student achievement in science requires that teachers have thorough content knowledge and receive pedagogical practice development. Curriculum that integrates standards based STEM learning experiences and exposes students to career clusters improves student engagement and achievement in science (Mason et al., 2012). Exposing students to real world science applications through student centered learning experiences improves their ability to understand complex scientific processes. The Western Wisconsin STEM Consortia Project outlines the critical components to improve student achievement in science. Students that were exposed to real world science applications through student centered learning experiences improved their ability to understand complex scientific processes. These learning performances, together with career connections, allow students to begin to explore post-secondary options in science careers that they may not have considered. Exposing students to science applications and connecting content to careers can generate motivation for learning. Teaching and Learning The quest for reducing the achievement gap and promoting our position as a world leader drove progressive education reforms of the mid-1900s. Providing students with rich educational experiences had become the focus of the national movement to improve student achievement. The quality of teachers is one of the greatest indicators of student success (Marzano, 2007; Rivkin, Hanushek, & Kain, 2005). The United States Department of Education’s “A blueprint for reform: The reauthorization of the Elementary and Secondary Education Act” (2010), made teacher development a priority. 28

Students have come to school with a variety of challenges in their lives. A highly effective teacher makes a greater impact on student achievement than the hindrances imposed by low socio-economic status (Nye, Konstantopoulos, & Hedges, 2004). Many authors have worked to define the instructional practices that promote student learning (Anderson & Krathwohl, 2001; Danielson, 2007; Hattie, 2008; Krathwohl, 2002; Marzano, 2007; National Research Council, 2000; Willis, 2006). Student achievement gains are directly connected to the level of content learning objectives, teachers’ content knowledge and pedagogical skill, and student engagement.” These school improvement strategies are effective because they are observed in action within the instructional core (City, Elmore, Fiarman, & Teitel, 2009, pp. 21-38). Standards Standard-based reform was born out of the sense of urgency created by the fall of the U.S.’s standing as world leaders. The U.S. Department of Education (1983) report, A Nation at Risk (The National Commission on Excellence in Education, 1983) and the 2002) legislation called for the introduction of standard-based curricula and accountability measures through the implementation of state-wide assessments (Devlin, Feldhaus, & Bentrem, 2013). Low test scores and low numbers of students entering science and engineering fields has led the U.S. to being ranked near the bottom in international ranks of students’ science and mathematics knowledge (Baldi, Jin, Green, & Herget, 2007; De Witte & Van Klaveren, 2014). State-based standards provides guidelines for states like Connecticut to follow in the development of local curricula. This core curricula became the framework for the Connecticut Mastery Test (CMT) and Connecticut Academic Performance Test (CAPT) 29

(Connecticut State Department of Education, 2004). Requirements were set forth by NCLB that included specific benchmarks that students must make adequate yearly progress (AYP). Districts that did not meet AYP requirements were labeled deficient or in need of improvement. Proficiency-rate benchmarks increased each year, resulting in an increased number of schools that did not meet AYP (Coleman, 2011). The Race to the Top program was the response to the problems of NCLB. Race to the top was a competitive program that required states to compete for federal funding. Priorities included developing new teacher and administrator evaluation programs, databased improvement of instructional practices, performance-based standards that emphasized depth over breadth and assessments that monitored student progress (United States Department of Education, 2009). Standards that resulted from Race to the Top were the Common Core State Standards (CCSS) (2014) and The Next Generation Science Standards (2015). Connecticut adopted CCSS in 2010 and is currently considering to adopt or adapt NGSS (Connecticut State Department of Education, 2014a, 2014b). The Next Generation Science Standards addresses the problem that too few students enter science careers. These standards, designed with stakeholders in science and science education, developed guidelines on what every public school graduate should know (Achieve Inc, 2015). These standards created a rich K-12 science curriculum and learning goals and resulted in a program that had a comprehensive learning experience for students. The NGSS eight practices of science and engineering require students to problem solve and argue from evidence (Achieve Inc, 2015). Teachers need intense professional development to expand their argumentative pedagogy. Analyzing student 30

work and providing constructive feedback helps students developing reasoning from evidence (McNeill & Knight, 2013). Curricula Curricula was intended to ensure learning outcomes are accomplished (Glatthorn, Boschee, Whitehead, & Boschee, 2011). An effective curriculum that impacts the instructional core was comprehensive and aligned to rigorous and relevant standards (Ainsworth, 2011; Wiles & Bondi, 2014). Engaging curricula provides authentic learning experiences, relevant life situations, is interdisciplinary, embeds information technology is motivational, thought provoking, rigorous and has collaborative and individual work (Ainsworth, 2011). A rigorous curriculum refers to the complexity of tasks not the degree of difficulty in completing them. Blackburn (2013) describes the differences between rigorous tasks and difficult tasks, quality not quantity, for everyone, not just elite students, and that learning is not a punishment. Rigorous tasks need support from curricula, instruction and assessment. Curricula facilitates the conceptual development that leads to deep learning; instruction creates student centered, engaging activities; and assessment ensures that the intended learning takes place for each student (Blackburn, 2013; Blackburn & Williamson, 2009; Mooney & Mausbach, 2008; Wiles & Bondi, 2014). Teacher professional development best practices are described as ongoing support and feedback that is rooted in school culture and use measures of student learning (Archibald, Coggshall, Croft, & Goe, 2011). Professional learning communities (PLCs) help facilitate a culture of learning with a focus on student achievement. Organizations that create strong learning cultures have a deep commitment to learn (Schein, 2010). 31

District and school-based leadership need to model, supervise and promote PLCs to ensure that learning activities target student needs. Teachers can use curricula mapping tools to develop and align essential skills and assessments that result in engaging experiences for students. Discussions that resulted from aligned instruction by mapped curricula help teachers modify instruction, creating properly scaffold supports. Supporting diverse learners promotes their engagement in the learning process (Jacobs, 2004). Instructional Best Practices Effective school improvement models that were successful in promoting student achievement directly affected the instructional core. The instructional core is comprised of the teacher, the student and the intended learning content (City, Elmore, Fiarman, & Teitel, 2009). The components of the instructional core are also influenced by psychological factors that affect student engagement (Corso, Bundick, Quaglia, & Haywood, 2013). Instructional strategies that motivate students are characterized by lessons that were aligned with students’ learning needs through the teacher’s use of a variety of teaching strategies, coupled with individualized instructional support, which allows student choice in learning linked to students’ experiences (Cooper & McIntyre, 1996, p. 158). Effective practices that promoted student engagement clustered around the themes of competence and control, value of education, and belonging (National Research Council and the Institute of Medicine, 2003). Planning instruction for engagement is supported by unit plan “blueprints” (Ainsworth, 2011; Jacobs, 2004; Strong, Silver, & Perini, 2001; Wiggins & McTighe, 2005; Wiles & Bondi, 2014). Five learning experience elements have been suggested as 32

ways to help students construct knowledge and foster engagement. Knowledge anticipation captures students’ attention and links learning to prior knowledge. Knowledge acquisition allows students to experience learning first hand (Llewellyn, 2007; Minner, Levy, & Century, 2010; Silver & Perini, 2009; Strong, Silver, & Perini, 2001). Capturing students’ attention and allowing them to learn through student centered experiences rather than direct instruction improve knowledge retention and lay the foundation for building self-efficacy. Practice and process allows students to learn deeply through modeling and coaching (Blackburn, 2013; Raphael, Pressley, & Mohan, 2008; Reiser, 2004; Silver & Perini, 2009). Knowledge application allows students the opportunity to demonstrate learning through summative and formative tasks (Silver & Perini, 2009; Wiggins & McTighe, 2005). Knowledge application with modeling and coaching supports enable students to explore the content more deeply and higher levels of complexity. Complex learning can be frightening for struggling students who lack supporting skills required to complete a learning task. Scaffolded instructional practices can reduce the fear by supporting students’ skills that are not the primary learning objective. Finally, reflection allows students to personalize learning and form generalizations and set learning goals (Miranda & Hermann, 2012; Silver & Perini, 2009; Zion & Mendelovici, 2012). Increasing Student Engagement Understanding how students become engaged in learning is just as much about the instructional process but also about the psychology of behavior. B.F. Skinner (1953), Thorndike (1913) and Kimble (1967) described human behavior as the actions in response to an individual’s perceived needs. Using principles of balance with the world 33

and individual goals by Piaget (1954) and Glasser (1981), researchers have begun to investigate how influencing the learning context, instructional strategies, and relationships with adults and peers, impacts student engagement. Student learning depended not just on what was taught, but how it was taught. Innovative pedagogical strategies such as project-based, inquiry and constructivist instructional approaches promote intrinsic student engagement. These rich learning styles helped students apply knowledge to everyday experiences (Krajcik, McNeill, & Reiser, 2008). Learning in this way better prepared students for future careers that are not apparent yet. Teacher’s depth of pedagogical content knowledge directly impacts the effectiveness of inquiry-based instructional practices. Teachers who lacked extensive skills and knowledge struggled to promote student engagement in inquiry-based activities (Kanter & Konstantopoulos, 2010). For engagement to have maximal impact on student achievement, it needs to span the dimensions of emotion, cognition and voice. On-task behavior, positive emotions, and personal voice characterize student engagement (Reeve, 2006). Marzano suggests five target areas to increase student engagement: high energy, missing information, the self-system, mild pressure, and mild controversy and competition (Marzano, 2007). Boosting student energy increases students’ ability to focus on academic activities (Marzano, 2007). Physical activity boosts blood oxygen levels and blood flow to the brain. The area of the brain that engages in movement is also the area that engages in learning (Jensen, 2005, p. 62). Daily physical activity increases behavioral engagement in both typically engaged students and students identified as at risk for disengagement (Barros, Silver, & Stein, 2009; Mahar et al., 2006). 34

Marzano describes humans as naturally inquisitive, and their preferences for solving problems or puzzles demonstrates that aspect of behavior (Marzano, 2007). Individuals take comfort in their ability to predict what will happen. This prediction mechanism leads to feelings of control over their environment and learning (Wiener, 1954). Instructional strategies such as project-based learning (PBL) and inquiry learning strategies allow students to utilize their natural inquisitive minds to problem solve (Ferreira & Trudel, 2012; Oliveira et al., 2013). The development of the sense of self refers to students’ perceptions of selfefficacy, identity, positive relationships and autonomy for learning (Marzano, 2007). Enjoyment of activities, it is suggested, requires eight components: probability of task completion, ability to concentrate on task, clear task goals, immediate feedback, deep task involvement without distraction, control over task, lack of concern over one’s self, and loss of sense of time (Csikszentmihaly, 2009). Deep engagement in learning requires intrinsic motivation and enjoyment of learning that is not dependent on external influences for continued learning efforts (Deci & Ryan, 2000; Skinner, Wellborn, & Connell, 1990; Zimmerman, 1997). Mild stress that creates a sense of urgency has been found to enhance memory and increase task focus (Cahill, Gorski, & Le, 2003; Shors, Weiss, & Thompson, 1992; Van Honk et al., 2003). Safeguarding against heightened stress is essential to prevent disengagement. Strategies such as feedback, student control of learning, positive studentteacher relationships, and structured learning environments keep stress at appropriate levels to optimize learning (Whitman, Spendlove, & Clark, 1986). Ensuring that tasks are of an appropriate level of complexity by differentiating instruction and scaffolding 35

tasks will keep students from becoming overly frustrated and disengage from the learning task (Quintana et al., 2004; Reiser, 2004). Overly difficult tasks push students to use coping strategies that protect them from what they perceive as harmful. Learned helplessness is one way that students cope when they do not have sufficient self-efficacy motivation to complete a task (Butkowsky & Willows, 1980; Dweck, Davidson, Nelson, & Enna, 1978; Peterson, Maier, & Seligman, 1993). Competition and mild controversy such as structured debate or academic games improve student engagement (Jensen, 2005). These activities elicit an emotional response, which increases focus on learning tasks and improve memory (Cahill, Prins, Weber, & McGaugh, 1994). Having students defend differing opinions through sustained discussion adds excitement to instructional activities and builds self-efficacy through mild pressure and cooperative learning (Good & Brophy, 2008; Reeve & Deci, 1996). Precautions to prevent embarrassment of teams or individuals must be taken or students’ self-efficacy will be diminished and reduce motivation to learn (Epstein & Harackiewicz, 1992; Moriarty, Douglas, Punch, & Hattie, 1995; Reeve & Deci, 1996). High-quality instruction and caring teachers foster student engagement (Raphael, Pressley, & Mohan, 2008). High-level instructional objectives compared to low-level instructional objectives increases student engagement for both students identified as at risk and the general student population (Downer, Rimm-Kaufman, & Pianta, 2007). Bloom’s (1956) taxonomy indicates that more complex learning and engagement occurs when creativity and creation are involved. Connecting creativity and interest together creates motivating complex and challenging learning tasks (Strong, Silver, & Robinson, 1995). 36

Rigorous instruction is not difficult for students; it is engaging and motivating (Tovani, 2011). Increasing rigor requires raised levels of content, increased complexity of tasks, appropriate support and guidance, open-ended tasks and higher expectations (Blackburn, 2013). Rigorous instruction can be used for all learning and ability levels. Scaffolding by adding structure to open-ended tasks, alternate source reading, graphic organizers, the use of technology and software tools enables all students to perform rigorous tasks (Blackburn, 2013; Blackburn & Williamson, 2009; Quintana et al., 2004; Reiser, 2004). Leaders should ensure that rigorous tasks are not mistaken for difficult tasks. These misunderstood results in struggling students are perceived as unable or unwilling to perform rigorous tasks. The frustration that students experience from challenging tasks may be enough to catalyze the disengagement process. Improving Teacher Quality Recent studies have supported that teacher evaluations improve teacher quality (Maslow & Kelley, 2012). High quality teachers produced student achievement gains equivalent to five to six months of learning each. These “irreplaceables” left schools at a similar rate as low-performing teachers. High quality teachers that had left their school reported that the district made little attempt to retain them. Districts made almost no effort to urge low-performing teachers to leave and ironically encouraged many to stay because replacing teachers can be difficult in struggling schools. Districts that retained higher performing teachers gave them frequent, positive feedback that helped identify areas in need of improvement. Additionally, these districts recognized teachers’ accomplishments publicly and informed those teachers they were high performing.

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Districts that applied two or more of these practices were able to retain high-performing teachers twice as long as districts that did not (Jacob, Vidyarthi, & Carroll, 2012). Student performance is an indicator that distinguishes a low performing teacher from a high performing teacher. Student performance accountability for teachers relies on accurately measuring student progress. Districts that incorporated student growth into their teacher evaluation systems needed to identify valid and reliable measures for teachers. Valid and reliable measures of student growth measure the impact of all teachers and support personnel that work with a student. Difficulties existed in measuring student growth in areas where a standardized assessment is not given (Goe & Holdheide, 2011). Weaknesses and strengths in teachers can be identified through teacher evaluations (Goe, Biggers, & Croft, 2012). Identifying specific skills that a teacher lacks and/or wants to acquire improves instructional outcomes; however, evaluating teachers is a method that has been the focus of intense scrutiny. Public perception is that tenured teachers can deliver substandard instruction without risk of job termination. Connecticut used research-based, national standards to design the System for Educator Evaluation and Development (SEED) model (Danielson, 2007; System for Educator Evaluation and Development, 2014; The New Teacher Project, 2012). This model defined four categories of teacher performance: student learning, teacher performance and practice, parent feedback and school-wide student learning or student feedback. The SEED model uses elements that included student achievement data, classroom observations and student surveys. These instruments identify and predict teachers’ ability to help students learn, diagnose strengths and weaknesses in classroom 38

practice and assess classroom culture. To facilitate growth in instructional practice, SEED promotes professional dialogue about student learning, professional development, coaching and feedback. The rubric and evaluator training ensures valid and reliable measures of teacher performance in a feasible manner (System for Educator Evaluation and Development, 2014). Addressing pedagogical weaknesses is often limited to teachers who are under intensive assistance programs. To support continuous teacher learning, professional development must be sustained over time, directed towards teacher’s needs and directly involve teachers through collaborative experiences (National Research Council, 2000). The “one and done” model of professional development that many districts implemented to fill required professional development days has not shown to have significant effect on student achievement (Desimone, Porter, Garet, Yoon, & Birman, 2002). Teachers were not likely to use newly acquired skills or work through challenges if they did not have the continuous professional development opportunities to help them implement new ideas in the classroom (Archibald, Coggshall, Croft, & Goe, 2011). Strong professional development helps teachers translate instructional best practices into the context of their classroom, thereby increasing student achievement (Elmore, 2008). School leadership should identify areas of low engagement and professional development target areas. Professional development aligned to teacher needs evaluations, and federal, state, district and school initiatives have the greatest impact on improving teacher quality (Schein, 2010). Districts that created a culture of life-long learning for all facilitated the professional growth of teachers. Teachers that were provided with opportunities and resources that supported continuous learning led to greater student achievement gains. 39

Teachers can build their content knowledge thorough the analysis of instructional units (Loughran, Mulhall, & Berry, 2012). Professional development effectiveness is improved when it includes a focus on content, uses teacher collaboration to examination of student work. These active learning strategies for teachers require feedback and follow-up. Professional development geared toward the taught content and how to convey it to students in meaningful ways are most likely integrated into daily classroom instructional practices. Collaborative examination of student work analyzed student-learning outcomes and determined if the intended instruction led to actual student learning. Working with colleagues helped teachers learn from each other. Active learning that provides teachers the opportunity to reflect on instructional practices to identify areas for development is essential while they apply new teaching practices. Teachers that implemented said practices needed ongoing mentor and coaching support and consistent feedback to master the newly acquired skills. Professional development that is on-going develops teachers skills while in the midst of implementing new instructional programs and allows new instructional skills to be applied in the classroom (Ingvarson, Meiers, & Beavis, 2005). High-level cognitive skills include critical thinking, problem solving, creativity and logical thinking. Discovery learning, problem-based learning, and constructivist learning were examples of some instructional strategies that promote high-level cognition. When compared to direct instruction, minimally guided instruction practices were far less effective at conveying content knowledge. These strategies negatively impacted students through the formation of misconceptions and incomplete or disorganized knowledge (Kirschner, Sweller, & Clark, 2006). Minimally guided 40

instruction provided extensive active learning opportunities for students. Active learning instructional strategies had substantial evidence that it supported student engagement and active learning (Adamson, Santau, & Lee, 2013; Bachelor, Vaughan, & Wall, 2012; Markwell, 2004; Pickens & Eick, 2009; Yuruk, Beeth, & Andersen, 2009; Zion & Mendelovici, 2012). The use of these instructional strategies require increased teacher time to plan and implement. School leadership needs to ensure that teachers have time and resources to plan, use, and receive feedback on the planning and implementation of minimally guided instructional strategies. Instructional objectives are the description of a student performance that the teacher intends to achieve with a lesson. Objectives ideally are described as specific, outcome-based, and measurable. Instructional objectives described the learner's behavior after instruction (Mager, 1997). Research developed hierarchy of instructional objectives through multiple taxonomic frameworks focused not just upon the content of learning but the modality of the student to learn, apply knowledge and problem solve. Successful learning outcomes stemmed from rich classroom experiences. Student achievement gains results from engaging, student-centered content that builds in complexity and difficulty (Anderson & Krathwohl, 2001; Bloom, 1956; Hess, Jones, Carlock, & Walkup, 2009; Mosston & Ashworth, 1990; Webb, 1997, 1999). Bloom’s Taxonomy defined instructional objectives by cognitive domains. These cognitive domains described the level of learning required by the student. High-level cognitive dimensions exists at the top tier, and low-level cognitive dimensions at the bottom. These high cognitive dimensions require learners to interact with content in ways that are more sophisticated. The problem solving process, for example, requires 41

students to analyze, evaluate and create solutions (Bloom, 1956; Krathwohl, 2002). All ability levels of students can accomplish high levels of rigor and complexity. The revised taxonomy of educational instructional objectives was the interaction of the cognitive processes dimension and the knowledge dimension. Taxonomic objectives were described as most useful at the unit level; however, they had valuable implications for learning activities and assessments as well (Anderson & Krathwohl, 2001, p. 105). The revised Bloom’s taxonomy linked instruction to student outcomes. The focus of learning is driven by educational instructional objectives, and instructional activities support the achievement of objectives. Objectives that are aligned to instructional activities achieved the intended learning outcomes (Anderson & Krathwohl, 2001, pp. 222-233). Webb’s Depth-of-Knowledge (DOK) examines the complexity of cognition for learning (Hess, Jones, Carlock, & Walkup, 2009; Webb, 1997, 1999). Level one DOK requires the student to recall information. Increased complexity that involved two or more steps raised the learning level to DOK level two. Level two involves the basic application of skills and concepts. High-level DOK involved the strategic and extended thinking to problem solve, make decisions, and solve complex problems from multiple sources across disciplines. DOK related more closely to the depth of content understanding and scope of a learning activity (Hess, Jones, Carlock, & Walkup, 2009). This model was more appropriately used for measuring planned instructional activities (Webb, 1997). Because of this, Webb’s DOK was integrated into the SEED model for teacher evaluation (System for Educator Evaluation and Development, 2014).

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Summary Student engagement is an important factor involved in the facilitation of student achievement. School leaders can use student engagement measures to identify students that are in early phases of becoming disengaging with learning. Monitoring students level of engagement may be just as, if not more important than test scores. The high correlation between student engagement and student achievement warrants continuous progress monitoring of students’ engagement levels. Understanding the factors that influence student engagement (behavioral, emotional and cognitive engagement) help educators develop intervention plans to address deficiencies in student engagement. School leaders need to provide professional development support for teachers to develop pedagogical skills and revise curricula and address the psychological factors that influence student engagement and motivation to learn influence student engagement levels. The intent of this research study is to determine the relationships of factors that influence the dimensions of student engagement and to inform educators in targeting areas to augment student engagement and therefore promote student persistence in science education.

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CHAPTER 3 METHODOLOGY This chapter details the methodology that was used to examine the factors that influence student engagement in middle school science. The methodology comprised of the research design, target population descriptors and sampling procedures, instrumentation and procedures for collecting and analyzing data. Research Question 1.

What is the relationship between behavioral student engagement in

science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? 2.

What is the relationship between emotional and cognitive student

engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? Hypotheses H01: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will not have a statistically significant relationship with behavioral student engagement. HR1: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will have a statistically significant relationship with behavioral student engagement. 44

H02: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will not have a statistically significant relationship with emotional and cognitive student engagement. HR2: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will have a statistically significant relationship with emotional and cognitive student engagement. Research Design A correlational research design was used to develop an understanding of the relationship between a student’s engagement in science and the level of instructional objectives, teacher efficacy, student grade level, and student achievement (see Figure 5). The dependent variables in this correlational study were the measures of student engagement as defined by the total score from the student survey and the percent of observations indicating passive or active engagement. Teacher efficacy (TSES), student grade level, and achievement scores (5th grade CMT) were the dependent variables. Survey and observational instruments will be used to include both observable (behavioral engagement) and non-observable (emotional and cognitive engagement) components of student engagement (Appleton, Christenson, & Furlong, 2008; Fredricks, Blumenfeld, & Paris, 2004; Wang, Willett, & Eccles, 2011).

45

Student Engagement BOSS and SAQ

Content Instructional Objective Level

Student CMT Score & Grade Level

Teacher Self-Efficacy

Figure 5. Alignment of research design instruments with theoretical framework. . Most classroom-level measures of engagement are observational measures of behavioral engagement (Fredricks et al., 2011). Student engagement is a meta-construct of behavioral (Connell & Wellborn, 1991; Finn, 1989), emotional (Connell & Wellborn, 1991; Finn, 1989) and cognitive dimensions (Butkowsky & Willows, 1980; Fredricks, Blumenfeld, & Paris, 2004). Dual measurements of engagement address the following concerns: (i) classroom level measures of student engagement do not measure all three dimensions and (ii) that the sole use of student self-reports may not accurately reflect the level of engagement. Student observational measures of engagement primarily measure behavioral engagement while the student survey reports emotional and cognitive engagement (Appleton, Christenson, Kim, & Reschly, 2006; Garcia & Pintrich, 1996). The two measures of student engagement were independently correlated with the five independent variables to better understand how they influence student engagement. This clarified the criterion validity for measuring student engagement at the classroom level. Survey methods can more quickly measure a range of student engagement than singlestudent observation methods. If both instruments co-vary with the same variables at all 46

age levels, it would suggest that the survey instrument alone would allow educators to accurately and easily measure engagement for curricular and instructional feedback. Target Population and Sample The target population for this study is middle school science students. Middle school students were chosen because research identified this age group as a turning point of science engagement. Students report that while they enjoy science, they do not consider STEM careers (Archer et al., 2010; Archer et al., 2013). During adolescent development students begin to identify with preferred subject areas and exclude nonpreferred subjects. Students seem to develop reluctance to study between 6th and 8th grades and begin to describe science using fearful terms (Yamamoto, Thomas, & Karns, 1969). The Quinnipiac School District1 serves a suburban community of approximately 24,000 with a median household income of $86,000 and 3.9% living in poverty (United States Census Bureau, 2015). The school district comprises of one PK-5 elementary school, three K-5 grade elementary schools, one 6-8 grade middle school and one 9-12 grade high school. Students were selected from Quinnipiac Middle School1 (QMS), a suburban middle school in Connecticut (DRG D). The selection of Quinnipiac Middle school was out of convenience for the researcher. Enrollment in QMS was 773 students on October 1, 2012. Teachers have on average of 14.9 years in education as compared to 14.3 in the state. The percent of teachers with a Master’s Degree or above was 77.8% as compared to the state at 80.3% (Appendix G). Currently the District Improvement Plan

1

Pseudonym

47

(DIP) focuses on planning for instruction, and the district has recently begun instructional walkthroughs for teachers. The goal of the walkthrough is to focus on the complexity level of instruction, teacher verses student-centered lessons, and evidence of student engagement. Sampling Procedures All students attending QMS were invited to participate in the research study. The building principal sent an e-mailed and hardcopy of the invitation to parents. A follow-up phone call home (robo-call by principal using School Messenger) reminded families to return the consent forms. A parent and the student participant signed informed consent and assent documents (Appendix H). Students that elected to participate will be randomly selected using a generated random number list for the BOSS observation protocol from each of the 45 science classrooms. Four students in each of the nine teachers’ five classes were identified. Gender, CMT assessment year, free/reduced meal status and CMT scores obtained from the CMT score database (Table 1) were used to compare the school population to the study participant sample to ensure the selected sample represents the school population (Connecticut State Department of Education, 2014, June 14).

48

Table 1. CMT Scores at QMS: Disaggregated Population Statistics Group

Number Tested

Average Scale Score

Percent at or above Goal

Percent at or above Proficiency

2010 District

274

271.2

74.1

92.7

Male

125

276.4

76.0

92.0

Female

149

266.8

72.5

93.3

F/R Meals

24

246.1

37.5

87.5

Full Price

250

273.6

77.6

93.2

2011 District

259

262.4

69.1

87.6

Male

141

261.1

70.2

87.2

Female

118

263.9

67.8

88.1

F/R Meals

26

235.3

34.6

76.9

Full Price

233

265.4

73.0

88.8

2012 District

253

268.0

74.7

92.5

Male

135

268.3

75.6

90.4

Female

118

267.7

73.7

94.9

F/R Meals

35

256.3

65.7

88.6

Full Price

218

269.9

76.1

93.1

49

2013 District

279

265.5

71.3

89.6

Male

146

268.8

74.0

90.4

Female

133

261.9

68.4

88.7

F/R Meals

37

239.9

56.8

78.4

Full Price

242

269.4

73.6

91.3

Note. Obtained from “Data interaction for Connecticut Mastery Test, 4th generation” by Connecticut State Department of Education. (2014, June 14). Retrieved from: http://ctreports.com Instrumentation The four instruments to assess the research question and evaluate the hypotheses were the Behavioral Observation of Students in Schools (BOSS), the Science Activity Questioner (SAQ), the Teachers’ Sense of Efficacy Scale (TSES), and the 5th Grade Science Connecticut Mastery Test (CMT). The BOSS and SAQ measure the dimensions of student engagement; the TSES measures teachers’ self-efficacy; and the Science CMT assesses the students’ achievement level in science (Table 2 and Figure 6).

50

Behavioral Engagement

• BOSS

Cognitive Engagement

• SAQ

Affective Engagement

• SAQ

Figure 6. Alignment of research design instruments with dimensions of student engagement. Table 2. Instrument Sampling and School Population Sizes Target

Number of Variables

Sample Size

Science Activity Questionnaire

6

180 (23.3%)

Behavioral Observation of Students in School

5

180 (23.3%)

Teachers’ Sense of Efficacy Scale

3

9 (100%)

Instrument

The BOSS instrument is an observational tool that measures student engagement behaviors. This tool uses the principle of systematic observation to determine the frequency and level of specific behaviors. This instrument was originally developed to screen students for Attention Deficit Hyperactivity Disorder and Oppositional Defiant 51

disorder in a variety of school based settings (DuPaul et al., 2004; Hintze & Matthews, 2004; McQuillan, DuPaul, Shapiro, & Cole, 1996; Volpe, DiPerna, Hintze, & Shapiro, 2005). These medical conditions result in increased occurrences of off-task behaviors. The BOSS measurement is used to determine patterns of behavior in these children to create intervention programs and evaluate the effectiveness of the intervention plan (DuPaul et al., 2004; McQuillan, DuPaul, Shapiro, & Cole, 1996; Shapiro, 2010; Vile Junod, DuPaul, Jitendra, Volpe, & Cleary, 2006; Volpe, DiPerna, Hintze, & Shapiro, 2005). The cause of off-task behavior that is the focus of this study is not medical. Shapiro (2010) summarizes a wide array of instructional intervention strategies that improve achievement with students with ADHD and ODD and students with academic problems that can be assessed using systematic direct observations such as the BOSS instrument. The monitoring of observed student engagement behaviors has been used to measure the effectiveness of learning intervention programs to improve student access to instructional objectives. Effective intervention plans that improve student engagement lead to improved student academic performances (Shapiro, 2010). The BOSS instrument reports findings as the percentage of occurrences of target behaviors out of the total number of observations. The stability of the student engagement measurement is improved with increased observation intervals. Measurements by trained observers have resulted in valid measurements of student engagement (Table 3) as determined by the inter-rater reliability calculations, construct validity, and criterion related validity measures (Fredricks et al., 2011). The inter-rater agreement was measured consistently high (Volpe, DiPerna, Hintze, & Shapiro, 2005). The BOSS instrument was able to discriminate children with ADHD from their typical 52

peers. (DuPaul et al., 2004; Vile Junod, DuPaul, Jitendra, Volpe, & Cleary, 2006). Student grade level limits criterion validity when comparing the correlation between BOSS measurements of on-task time and a self-report measure of student effort and persistence. Significant correlation between these factors were found at 4th grade but not 3rd. It was suggested that younger students may not be able to accurately self-report their engagement in learning tasks (Spanjers, Burns, & Wagner, 2008). Table 3. Validity Measures for Behavioral Observation of Students in School Reported Inter-rater Agreement

.93-.98

Construct Validity

Discriminated children with ADHD from their nondisabled peers 3rd Grade: r(56) = -.15, p = .25

Criterion Related Validity 4th Grade: r(61) = .30, p < .05 Note. Obtained from “Measuring student engagement in upper elementary through high school: A description of 21 instruments. Issues & answers. (REL 2011-No. 098)” by Fredricks, J. A., Mccolskey, W., Meli, J., Mordica, J., Montrosse, B., & Mooney, K. (2011). Retrieved from http://ies.ed.gov/ncee/edlabs/regions/southeast/pdf/ REL_2011098.pdf The SAQ (Appendix K), as obtained from Miller (1991), was used to assess student self-reported emotional and cognitive engagement levels. This instrument assesses the students’ self-reported level of engagement in a science lesson. The six-goal scale items identified (Table 4) by Meece, Bluemenfeld, & Hoyle were mastery orientation, ego/social orientation, work-avoidant, affiliative goals, active learning and superficial learning (Meece, Blumenfeld, & Hoyle, 1988). 53

Table 4. Reliability of Goal Scale Items for Science Activity Questionnaire Cronbach's Alpha Coefficient Mastery Orientation

.94

Ego/Social Orientation

.85

Work-Avoidant

.77

Affiliative Goals

.75

Active Learning

.87

Superficial Learning

.79

Note. Obtained from “Effects of hands-on, activity-based science and a supportive instructional environment on at-risk sixth-grade students' attitude toward science, achievement in science, goal orientation, and cognitive engagement in science.’ by Miller, A.-C. S. (1991). Retrieved from ProQuest Dissertations & Theses Full Text database. (303872265) Teachers’ Sense of Efficacy Scale (Appendix M) measures teachers’ sense of efficacy in the areas of student engagement, use of instructional strategies, and effectiveness of classroom management strategies. The overall validity of this instrument (Table 5) is indicated by the Cronbach's alpha coefficient value of .94, and each factor loading ranged from .87-.91 (Tschannen-Moran & Hoy, 2001).

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Table 5. Reliability Measures for Teachers’ Sense of Efficacy Scale Mean

SD

Cronbach's Alpha Coefficient

Efficacy in Student Engagement

58.4

8.8

.87

Efficacy in Instructional Strategies

58.4

8.8

.91

Efficacy in Classroom Management

53.6

8.8

.90

Note. Obtained from “Teacher efficacy: Capturing an elusive construct.” by TschannenMoran, M., & Hoy, A. W. (2001) Teaching and Teacher Education, 17, 783-805. Retrieved from http://dx.doi.org/10.1016/S0742-051X(01)00036-1 The fifth grade science CMT results for each year was used to determine the students’ science achievement level. This measurement is preferred as a measure of students’ science achievement because the assessment is a more consistent and valid (Table 6) measure of student progress than teacher grades (Hendrawan & Wibowo, 2011, 2012, 2013a, 2013b). Teacher grades were not considered since they varied across levels and between teachers, and a standardized and reliable measure of student achievement was needed to compare impact on student engagement dimensions.

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Table 6. Reliability Statistics for the Science CMT Year

Mean

SD

Cronbach's alpha coefficient

2010

256.90

256.96

0.880

2011

257.70

257.76

-

2012

261.70

261.78

0.88

2013

260.10

260.10

-

Note. Obtained from “The Connecticut Mastery Test: Technical reports” by Hendrawan, I., & Wibowo, A. (2011, 2012, 2013a, 2013b). Retrieved from http://www.sde.ct.gov/ sde/cwp/view.asp?a=2748&q=334754 Data Collection Procedures Behavioral Observation of Students in Schools The BOSS instrument uses trained observers to collect observed student behaviors. Pearson Clinical and Shaperio, the publishers of the instrument, recommend a training protocol and measurement of the resulting inter-rater reliability (Shapiro, 2014). Trained BOSS observers (8-10) collected evidence of student behavioral engagement (Appendix I). Training took place within two weeks of observation period. Training was adapted from Pearson BOSS webinar. Each BOSS observer performed a mock observation of a class video (10 minutes) to calculate inter-rater reliability. If an inter-rater reliability (Krippendorff alpha statistic) of less than .8 was achieved, observers were debriefed on what they observed to recalibrate. Repeated mock observations were performed until the desired level of inter-rater reliability is achieved.

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Observations consisted of one session for each of the forty-five class sections. Class sections will include all sixth, seventh and eighth grade science classrooms. Two trained BOSS observers performed simultaneous collection of student engagement. The BOSS observer recorded the behavioral engagement of two identified students for 15 minutes each. To select students for BOSS observations, ten lists of random integers (1-30) were produced. A second random list, with values one through ten, was assigned to each Session ID. This list indicated the student selection list used for each Session ID. Participating students were ordered alphabetically, and the first number that matches a student was used. If the selected student was absent, the second integer was used. Each session was coded with a randomly generated five-digit session ID. The session ID numbers linked data collected across all instruments anonymously. Observational data was collected every 20 seconds. At each fifth interval, a peer was used (prompted by instrument) for measurement comparison. Each “peer” comparison was a combined measure of different students. BOSS observers began in opposite ends of the room and move systematically. Depending on classroom arrangement, “peer” students were selected by selecting a starting point within the class and working towards the rear of the class or clockwise. Teachers indicated the DOK level to the observer based on the flow chart (Appendix N) and the cognitive rigor matrix (Appendix O) provided at the conclusion of the lesson period. Session codes on the flow chart linked the DOK level to the Observation Session ID. This system ensured participant confidentiality and no teacher identifying record existed.

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Science Activity Questionnaire The SAQ instrument assesses students’ goal orientations for learning science (Meece, Blumenfeld, & Hoyle, 1988). This instrument was administered electronically using a pdf form using Adobe Forms Central (Appendix K). After the BOSS observers completed the session observations, the four students observed in each class completed the web based survey version of the SAQ. The observers recorded on the form the ‘Student Number,’ ‘Session ID,’ and ‘Teacher Number’ in the indicated fields. These fields allowed the researcher to link the independent variables for analysis while maintaining student and teacher anonymity. The SAQ used Likert scales to assess student goal orientations and engagement patterns. Survey administrators brought the four observed students to the media center to complete the SAQ survey. Using the Survey Administration Form (Appendix K) Survey proctors directed students to input the student, session and teacher codes and read the directions to the students. After students completed the SAQ survey, students submitted the document. This send data to the CognitoForms collection interface. The CognitoForms interface allowed the researcher to download the survey data to a .cvs file and import into IBM SPSS Statistics. Teacher’s Sense of Efficacy Scale The TSES measured each teacher’s sense of efficacy in the areas of student engagement, use of instructional strategies, and effectiveness of classroom management strategies using a nine point Likert Scale (Tschannen-Moran & Hoy, 2001). This instrument was administered electronically using a .pdf form (Appendix M). Teachers who elect to participate in the research study was emailed the web link to the TSES survey prior to the study. This email directed teachers to complete the 58

survey in a private location at their conveyance. Each teacher completed the survey and included the teacher identification number given at the beginning of the study. This field allowed the researcher to link the independent variables for analysis while maintaining teacher anonymity. Teachers submitted surveys using the web-based “Submit” button. This method did not collect e-mail addresses, teachers’ names, or other personal identifying information. Instrument Linking Protocol To ensure both student and teacher anonymity, sets of randomly generated numbers was used to link independent variables. The “Random Integer Generator” from Randomness and Integrity Services Ltd. (2014) was used to produce these numbers. The session IDs were created from five digit randomly generated numbers. The student numbers were three digit numbers used to anonymously link the session to the lesson DOK level, student BOSS observations, student SAQ responses, student CMT scores and student grade level. Preliminary Analyses To determine the inter-rater reliability of the BOSS instrument and ensure the reliability of observational data, an inter-rater reliability analysis was performed. The Krippendorff alpha statistic measured the observed and expected disagreement of raters: α=1−

Do De

Do is observed disagreement and De is expected disagreement based on chance. This statistic is considered to be the most reliable for more than three coders. The SPSS macro

59

KALPHA, will be used to calculate the Krippendorff alpha statistic (Hayes & Krippendorff, 2007). This study employed a multiple regression analysis to answer the research question. Field (2013) described two rules that suggest 10 or 15 cases per predictor. This study used eight predictor variables, which would require 120 participants. The intended selection of 180 study participants was an adequate sample size to answer the research question and evaluate the study hypotheses. Data Analysis Behavioral Engagement To answer the first research question and null hypotheses that measure behavioral engagement: 1.

What is the relationship between behavioral student engagement in science

and teacher self-efficacy, complexity level of instructional objectives, grade level and student achievement? H01: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, grade level and student achievement will not have a statistically significant relationship with behavioral student engagement. Independent Variables: Objective level, factor loading from the Teachers’ Sense of Efficacy Scale, student 5th Grade CMT scores, student grade level. Dependent Variable: Behavioral engagement measures. Emotional and Cognitive Engagement To answer the second research question and null hypotheses that measure emotional and cognative engagement: 60

2.

What is the relationship between emotional and cognitive student

engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? H02: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will not have a statistically significant relationship with emotional and cognitive student engagement. Independent Variables: Objective level, factor loading from the Teachers’ Sense of Efficacy Scale, student CMT scores, student grade level. Dependent Variable: Emotional and cognative student engagement measures.

Complexity Level of Instructional Objectives

Teacher Selfefficacy

Complexity Level of Instructional Objectives

Student Achievement

Teacher Selfefficacy

Grade Level BOSS Student Observation

Student Achievement

Grade Level SAQ Student Survey

Figure 7. Dual multiple regression of the independent variables to each measure of student engagement. The BOSS measures research question one and alternative hypotheses one, while the SAQ measures research question two and alternative hypotheses two. Preliminary Data Analysis To answer the research question and evaluate the null, this study utilized a multiple regression analysis to determine the correlation a linear combination of the

61

independent variables independent variable on each measure of student engagement. The resulting linear regression potentially serve as a predictor of student engagement. Multiple regression of independent variables include the objective level, factor loading from the teachers’ sense of efficacy scale, student CMT scores, student grade level. These independent variables measured the degree of correlation to behavioral engagement of the student using the sum of the occurrences of engaged observations using the BOSS instrument. To determine the correlation of the independent variables to cognitive and affective engagement levels, the students’ summative score from the SAQ instrument was used. Descriptive statistics describe the sample distribution’s mean, standard deviation and sample size. The sample size, and descriptive statistics guarded against input errors. Summary Understanding the factors that influence student engagement are essential to improving student success. Students that are not engaged in the learning process struggle to persist with challenging instructional activities. Modifying student engagement levels for students who are currently disengaged or at risk for disengagement will improve student achievement and increase the number of students pursuing STEM based careers.

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CHAPTER 4 RESULTS This chapter presents the results obtained using the correlational research design methodology. The chapter opens with a review of the purpose and design, including the research questions and hypotheses that guided the study. The second section presents an overview of the research methods. The chapter concludes with a presentation of the preliminary analysis and the correlational data analysis. Purpose and Design The purpose of this study was to understand the factors that influence student engagement in middle school science classrooms. A correlational research design was used to develop an understanding of the relationship between a student’s engagement in science and the level of instructional objectives, teacher efficacy, student grade level, and student achievement. The dependent variables were the measures of passive or active engagement (BOSS). Depth of knowledge (DOK) level, teacher efficacy in student engagement, instructional strategies, and classroom management (TSES), student grade level, and achievement scores (5th grade CMT) were the independent variables. Survey and observational instruments were used to include both observable (behavioral engagement) and non-observable (emotional and cognitive engagement) components of student engagement (Appleton, Christenson, & Furlong, 2008; Fredricks, Blumenfeld, & Paris, 2004; Wang, Willett, & Eccles, 2011).

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Research Question 1. What is the relationship between behavioral student engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? 2. What is the relationship between emotional and cognitive student engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? Hypotheses H01: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will not have a statistically significant relationship with behavioral student engagement. HR1: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will have a statistically significant relationship with behavioral student engagement. H02: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will not have a statistically significant relationship with emotional and cognitive student engagement. HR2: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will have a statistically significant relationship with emotional and cognitive student engagement.

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Participant Recruitment and Selection Participant recruitment began first by meeting with all teachers in small groups to explain the scope and objectives of the study. All teachers chose to participate in the study. Teachers were provided with informed consent documentation for their participation in the survey as well as informed consent and assent documents to distribute to their students. Each teacher in turn, explained to students the scope and objectives of the study. Students were provided consent and assent documents to return to school to indicate their desire to participate. Returned consent and assent forms were sorted by classroom. Classrooms with fewer than six participating students were visited by the researcher to further explain the study. This was done because teachers reported back that students were wary to participate because they had not met the researcher. Four students, and two alternates from each classroom were selected using random number tables to participate in the study. Classrooms with fewer than four students electing to participate did not have alternates if study students were absent. Using the dual recruitment strategy, approximately 20% of the QMS population elected to participate. Sample demographics were consistent with population demographics as indicated by the results of a Chi-Square goodness of fit test reported in Table 7.

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Table 7. Chi-Square Goodness of Fit Tests of Sample to Population (N=118) Pearson Chi-Square

df

Asymp. Sig. (2-sided)

Gender

.170

1

.680

Ethnicity

5.708

5

.336

Free or Reduced Meals

1.718

1

.190

Language

.009

1

.923

Special Education

1.761

1

.184

Demographic

Instrumentation and Data Collection Instructional objectives (DOK), student grade level and student achievement were used as predictor variables. To measure teacher self-efficacy, the Teachers’ Sense of Efficacy Scale (TSES) was used. All nine teachers completed the TSES and the mean and standard deviation of each scale is reported in Table 8. Teachers rated their selfefficacy lowest regarding student engagement (M=41.20, SD= 6.28) and rated their selfefficacy for instructional strategies (M=58.74, SD= 7.98) and classroom management (M=58.03, SD= 8.40) similarly and considerably higher. Standard deviations showed reasonable variation on each of the three scales. When compared to the published (see Figure 5) mean and standard deviation values of engagement (M=58.4, SD= 8.8), instruction (M=58.4, SD= 8.8), and management (M=53.6, SD= 8.8), the mean for student engagement was 17.2 less than published figures with a reduced standard deviation of 2.52. Instruction and management values were aligned with published figures (Tschannen-Moran & Hoy, 2001). The reduced standard deviation for student

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engagement has the potential to limit the explanatory power of this variable in subsequent correlational analyses. Table 8. TSES Scale Means and Standard Deviations (N=9) Mean

Standard Deviation

Efficacy in Student Engagement

41.20

6.28

Efficacy in Instructional Strategies

58.74

7.98

Efficacy in Classroom Management

58.03

8.40

Student grade level and Science CMT Scale scores were obtained from student records. The complexity of instruction as measured by the Depth of Knowledge Scale and flow chart (Appendix N). Forty-two classrooms were observed using the DOK scale. To understand how the factors of teacher self-efficacy, complexity level of instructional objectives, and student grade level influence student engagement multiple regression analyses were performed, one for behavioral and one for cognitive and emotional engagement. Research Question 1 1. What is the relationship between behavioral student engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? Multiple Regression Assumptions Prior to statistical analysis, the four assumptions for multiple regression were tested. These assumptions require 1) all predictor variables are linearly related to

67

outcome variables; 2) residual errors are normally distributed; 3) homoscedasticity, and 4) absence of multicollinearity. Female Students

Male Students

Figure 8. Scatterplot of independent variables with female and male behavioral engagement observation measures. Testing the first assumption of linearity, the researcher created a matrix of bivariate scatterplots for females and males (Figure 8). These scatterplots were examined for linearity with the dependent variables of observational engagement (active and passive engagement). Examination of the scatterplot matrices revealed reasonable levels of linearity between predictor and outcome variables as well as no indications of bivariate outliers. Pearson correlations (Table 9) between observed measures of active and passive student engagement were strongly significant for female students. However, correlations differed significantly for male students. Males were significant for DOK and active engagement (R = .24, sig. = .04), passively engaged with current grade (R= -.25, sig. =

68

.03) and DOK (R= -.34, sig. = .01). All other correlations were found to be insignificant. Therefore, following analyses addressed female and male analyses separately. Table 9. Pearson Correlations of Engagement with Predictor Variables by Gender (N=118) Current Grade

Efficacy in Student Engagement

Efficacy in Instructional Strategies

Efficacy in Classroom Management

Depth of ScienceKnowledge Scale Score Level

R

sig.a

R

sig.a

R

sig.a

R

sig.a

R

sig.a

R

sig.a

Actively Engaged in Task

.36**

.00

.22*

.05

.22*

.05

.27*

.02

.43**

.00

-.26*

.03

Passively Engaged in Task

-.38**

.00

-.33**

.01

-.33**

.01

-.38**

.00

-.46**

.00

.26*

.03

Actively Engaged in Task

.15

.13

.08

.29

.05

.35

.20

.07

.24*

.04

-.20

.07

Passively Engaged in Task

-.25*

.03

-.06

.33

-.04

.38

-.17

.11

-.34**

.01

.12

.20

Female

Male

a. 1-tailed *. Correlation is significant at the 0.05 level (1-tailed). **. Correlation is significant at the 0.01 level (1-tailed).

The second assumption required that residual errors were to be normally distributed. Examination of the distribution of the standardized residuals and the P-P plots (Figure 9 and 10) did not indicate any serious violations of the assumption of normality.

69

Female Students

Male Students

Figure 9. Normality of standardized residuals for passively engaged students.

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Female Students

Male Students

Figure 10. Normality of standardized residuals for actively engaged students. The third assumption was to examine the outcome variables homoscedasticity. To address this assumption and determine if there were any outliers, the standardized residuals from this regression were plotted (Figure 11) against the standardized predicted. These plots did not indicate the presence of any outliers and the scatterplots for female and male active engagement were determined to meet the assumption of 71

homoscedasticity. Passive engagement for both female and male students indicated some evidence of heteroscedasticity. Since violations of homoscedasticity must be quite severe to impact multiple regression, the levels were determined to be acceptable for this study (Statistics Solutions, 2013). Female Students

Male Students

Figure 11. Scatterplots of linearity and homoscedasticity for female and male students. Finally, to test for multicolinearity, a tolerance statistic was computed for each predictor variable. Multicolinearity results when two or more predictor variables are highly related. Analysis of the predictor variables indicated that none had a tolerance

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statistic less than .250. Values below .10 would indicate a potential for a serious multicolinearity problem (Stern, 2010). Sample size was the final consideration for reliability of the regression modeling. This study yielded 118 participants, 59 females and 59 males. This study met the common rule of thumb of 10 cases per predictor variable (Field, 2013). To meet this recommendation using the four predictor variables at least 40 female and male students (total 80) would be needed. Multiple Regression Results The first research question asked, “What is the relationship between behavioral student engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement?” A multiple regression analysis was performed to answer this question. For female and male students; student grade level, overall teacher self-efficacy scores, DOK of lesson, and 5th Grade CMT Science scale scores were entered as predictor variables and measures of active and passive engagement were outcome variables. The purpose of this analysis was to develop a predictive tool for each type of student engagement. The regression model summary (Table 10) contains the results for each regression model. For females, the regression model for active and passive engagement reports an effect size of 18% and 25% respectively. Conversely, for male students, the effect size was much lower. The model for male active and passive engagement had less predictive power. The effect size actively engaged was only 11% and passively engaged was slightly greater at 16%.

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Table 10. Regression Model Summary for Students Observed Engagement (N=118) Group

Dependent Variable Model R

Standard Adjusted R Error of the R Square Square Estimate

Actively Engaged in Task

.52a

.27

.18

10.08

Passively Engaged in Task

.58a

.34

.25

9.30

Actively Engaged in Task

.45a

.20

.11

10.03

Passively Engaged in Task

.50a

.25

.16

10.03

Female

Male

a. Predictors: (Constant), Science-Scale Score, Current Grade, Teacher Efficacy, Depth of Knowledge Level

As reported in Table 11, statistically significant results on were obtained for active [F(6,48)=2.91, p= 0.02] and passive [F(6,47)=3.95, p= 0.00] student engagement for female students and passive [F(6,49)= 2.71, p= 0.02] for male student engagement. A statistically significant effect, albeit close, was not found for male students for active [F(6,49)=2.07, p= 0.075] student engagement. These results indicated that the models were much better at predicting passive student engagement.

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Table 11. ANOVA Table for Students Observed Engagement Group

Sum of Squares

df

Mean Square

F

Sig. a

Regression

1814.31

6

302.38

2.91

.02*

Residual

4972.68

48

103.60

Total

6786.98

54

Regression

2047.61

6

341.27

3.95

.00*

Residual

4060.92

47

86.40

Total

6108.54

53

Regression

1248.91

6

208.15

2.07

.07

Residual

4925.02

49

100.51

Total

6173.93

55

Regression

1636.93

6

273.82

2.71

.02*a

Residual

4928.79

49

100.59

Total

6565.71

55

Dependent Variable Model

Actively Engaged in Task Female Passively Engaged in Task

Actively Engaged in Task Male Passively Engaged in Task

a. Predictors: (Constant), Science-Scale Score, Current Grade, Teacher Efficacy, Depth of Knowledge Level *. Significant at the 0.05 level (1-tailed).

The individual contribution of each predictor variable of active (AE) and passive (PE) engagement with current grade (GRD), teacher self-efficacy (TE), depth of knowledge level (DOK) and CMT science scale score (SSS) were measured. Judging by the beta coefficients and part correlations, female active engagement science the Science

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scale CMT score (B = -.28, sig = .05, sr = -.25) was significant. For passive engagement Science scale CMT score (B = .28, sig = .04, sr = .27) was also significant. Research Question 2 2. What is the relationship between emotional and cognitive student engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? Data Collection Procedures The SAQ (Appendix K), as obtained from Miller (1991), was used to assess student self-reported emotional and cognitive engagement in science. This instrument assesses the students’ self-reported level of engagement in a science lesson. The six-goal scale items identified (Table 4) by Meece, Bluemenfeld, & Hoyle were mastery orientation, ego/social orientation, work-avoidant, affiliative goals, active learning and superficial learning (Meece, Blumenfeld, & Hoyle, 1988). The survey uses three and four point Likert scales to measure each scale item. The students observed for research question one were the same sample used for this secondary measure of student engagement. After classroom observations were conducted, student participants were taken to the school media center to answer the survey questions using the CognitoForms survey interface. This interface required students to answer every question before submitting their survey responses. Results were compiled on CognitoForms secure database and downloaded as a *.cvs spreadsheet for subsequent import into IBM SPSS Statistics. The middle school media center specialist, using the protocol in Appendix K, administered the SAQ. While students were directed to answer based on the lesson they

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just experiences, there was no way to ensure students’ were not influenced by previous classroom experiences. Preliminary Analysis Computation of each goal scale item category followed the goal scale item factor structure of the published SAQ survey (Appendix K). Overall students reported most strongly perceptions of work-avoidant orientations (M = 2.25, SD = .73) and superficial learning (M = 2.59, SD = .41). Conversely, students rated active learning (M = 1.55, SD = .38) and mastery orientation (M = 1.62, SD = .47) lowest. Table 12. Mean and Standard Deviation of Science Activity Questionnaire (N=118) Mean

Standard Deviation

Mastery Orientation

1.62

.47

Ego/Social Orientation

1.99

.71

Work-Avoidant Orientation (reversed)

2.25

.73

Affiliative Goals

1.77

.65

Active Learning

1.55

.38

Superficial Learning (reversed)

2.59

.41

SAQ Total Scorea

8.59

1.61

a. Calculation: (mastery orientation, ego/social orientation and active learning) (work-avoidant and superficial learning)

The SAQ score value was computed using the sum of positive indicators of engagement (mastery orientation, ego/social orientation and active learning) and reverse coding the negative indicators of engagement (work-avoidant and superficial learning).

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The affiliative goals subscale was omitted from the composite score calculation due to concerns of ambiguity of the questions as discriminators or detractors of student engagement. The calculation protocol for combined emotional and cognitive engagement values was consistent with the protocol developed by Meece, Blumenfeld, and Hoyle (1988). As reported in Table 13, the SAQ total score and subscale scores’ mean and standard deviation indicated reasonable variation needed for multiple regression calculations needed to answer the second research question. Assumptions for Multiple Regression In the same way that the assumptions of multiple regression were tested for research question one, they were repeated for research question two using the composite score of student self-reported measure of emotional and cognitive engagement. Female Students

Male Students

Figure 12. Scatterplot of predictor variables with female and male student reported measures of emotional and cognitive engagement.

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Results of the bivariate scatterplot matrices for female and male student reported measures of emotional and cognitive engagement are shown in Figure 12. Examination of the scatterplots revealed reasonable levels of linearity between predictor and outcome variables. Additionally, there were no indications of bivariate outliers. Pearson correlations (Table 14) between female and male student self-reported measures of emotional and cognitive engagement and predictor variables were not statistically significant for both female and male students. Table 13. Pearson Correlations of Self-Reported Cogitative and Emotional Engagement with Predictor Variables (N=118) Current Grade

Efficacy in Efficacy in Efficacy in Depth of ScienceStudent Instructional Classroom Knowledge Scale Score Engagement Strategies Management Level

r

sig.a

r

sig.a

r

sig.a

r

sig.a

r

sig.a

r

sig.a

Female

-.13

.17

-.03

.40

-.07

.31

-.01

.46

-.13

.17

.07

.31

Male

.11

.21

-.02

.45

.08

.29

-.11

.22

.06

.34

-.02

.46

a. 1-tailed

The second assumption required that residual errors were to be normally distributed. Examination of the distribution of standardized residuals and P-P plots (Figure 13) did not indicate any serious violations of the assumption of normality.

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Female Students

Male Students

Figure 13. Normality of standardized residuals for female and male students’ selfreported engagement. The third assumption was to examine the outcome variables homoscedasticity. To determine if there were any multivariate outlier, the standardized residuals from this regression were plotted (Figure 14) against the standardized predicted values. These

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plots did not indicate the presence of any outliers and the scatterplots were determined to meet the assumption of homoscedasticity. Female Students

Male Students

Figure 14. Scatterplots of linearity and homoscedasticity for female and male students’ self-reported engagement. Finally, to test for multicolinearity, a tolerance statistic was computed for each predictor variable. Multicolinearity results when two or more predictor variables are highly related. Analysis of the predictor variables indicated that none had a tolerance statistic less than .17. Values below .10 would indicate a potential for a serious multicolinearity problem (Stern, 2010). Sample size was the final consideration for reliability of the regression modeling. This study yielded 118 samples, 59 females and 59 males. This study met the common rule of thumb of 10 cases per predictor variable (Field, 2013). To meet this reccomendation using the four predictor varables at least 40 female and male students (total 80) would be needed for a reliable multiple regression. Since it was determined

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that asumption of linearity was not met and not correctable with standard variable transformational procedures a multiple regression would not be an appropriate next step. Summary Analysis of the behavioral engagement for female and male students revealed significant correlations for female students. However, no significant correlations were found for male students. Regression modelling for female active and passive engagement found that science CMT scores significantly contributed to the models. No other predictor variable approached levels of significance in the models. The examination of emotional and cognitive engagement using the SAQ instrument did not satisfy the assumptions of linearity for multiple regression. Therefore, the predictor variables were not able to predict levels of student engagement.

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CHAPTER 5 DISCUSSION AND CONCLUSIONS The final chapter of this dissertation is comprised of five sections: Statement of the Problem, Review of the Theoretical Framework, Summary of Research Methods, Discussion of Findings, Conclusions and Recommendations for Practice. This chapter connects the results of this research study with the theories of the Student Engagement Core Model and theories of motivational development. Statement of the Problem The central problem this study investigates is that students need to be critical thinkers and problem solvers to be successful in life. The book The Flat World and Education by Darling-Hammond describes how entrepreneurs now have to compete for local business against global competitors. Barriers of distance have been dismantled by increases in communication technology. The limiting factor for businesses to reach potential clients is no longer geography. An estimated 85% of available jobs require specialized post-secondary training to keep up with the rapid pace of technological innovation and changing skill needs of companies (Wagner, 2014). The Partnership for 21st Century Skills has called for greater emphasis on students’ civic, academic and interdisciplinary, digital, and global competencies and understandings (Partnership for 21st Century Skills, 2014). Student skill deficits have disproportionally impacted poor and minority communities (Wagner, 2014). The skill deficits caused by educational 83

inequality reinforces racial isolation and perpetuates poverty. Economic changes that demand highly skilled and specialized workers have drastically limited job opportunities for un-skilled employment. Increasing high school dropout rates and resulting high unemployment rates have been linked to crime and welfare dependency (DarlingHammond, 2010). Monitoring student achievement and supporting schools to identify student learning difficulties and develop interventions has become essential to closing the achievement gap (Darling-Hammond, 2010). Most recent data shows that U.S. students might not be receiving the essential academic skills they will need to compete globally in their careers (National Science Board, 2014). Currently, eighteen percent of high school sophomore U.S. students do not meet basic proficiency levels in science. Comparing global academic achievement levels, U.S. students are ranked twenty-third in the world (Kelly, Nord, Jenkins, Chan, & Kastberg, 2013). U.S. fourth and eighth graders rank sixth and seventh respectively in the 2011 TIMMS achievement assessment (Provasnik et al., 2012). Students that are prepared for STEM careers will enable the U.S. to maintain its global competitiveness. Student achievement in science can be improved by focusing attention on critical thinking and problem solving skills. These skills will better prepare students for future STEM careers (National Research Council, 2000; National Research Council and the Institute of Medicine, 2003; National Science Board, 2014). School disengagement is hindering students ability to prepare for the careers of the future. The skills students need for the 21st century require authentic learning, mental model building, internal motivation, multiple intelligences and social learning (Trilling & Fadel, 2009). School leaders need to understand why some students begin to disengage 84

with school and go beyond measures of high stakes testing. Progress monitoring of student engagement and motivation may be an additional way that educators can design intervention strategies to meet students’ individual learning needs. The development of intervention strategies to halt the disengagement process will improve students’ persistence in science through high school and into post-secondary careers (Archer et al., 2013; Archer & Tomei, 2014; DeWitt, Archer, & Osborne, 2014). Since disengagement is a process, identifying early indicators of disengagement will be essential to successful intervention strategies. Supporting the self-efficacy of students by providing relevant, rigorous, and student-centered instruction is imperative to improve student outcomes and increase student identification with science (Achieve Inc, 2015). Review of the Theoretical Framework Student engagement is defined as the degree of attention, curiosity, interest, optimism, and passion that students demonstrate when they learn (Great Schools Partnership, 2014). Students’ commitment to, valuing of, and connection with the people, educational goals, and outcomes promoted by a school enhances their motivation to learn and persist in difficult tasks (Finn, 1989). This research study used the Student Engagement Core (SEC) Model (see Figure 1) as a framework for understanding the interactions between teacher efficacy, instructional objective complexity, student achievement, gender and student grade level on science student engagement. The SEC framework describes a phenomenon that when students and teachers build strong relationships, teachers have a high degree of competence, and content that students interact with is relevant, student engagement is fostered.

85

Summary of Research Methods Using the conceptual framework as a guide for developing a research method this study utilized a correlational research design method. Multiple regression calculations were performed to determine possible predictive measures of student engagement. Independent variables were chosen that had supporting research that suggested they influenced student engagement and student achievement. The theoretical framework was linked to each independent variable and the dependent variables (Figure 5). Instructional content was varied by differing instructional objective levels, teacher efficacy was the measure of each teacher’s perception of their effectiveness, and the student demographics and test scores described the student’s background and experience in science. Student engagement is divided into three subtypes, behavioral, cognitive and emotional. Since each element has different indications, the research study was divided into two research questions. The first research question focused on the observable measures of behavioral engagement, while the second research question delved into the students’ self-reported cognitive and emotional engagement. Research Question 1 1. What is the relationship between behavioral student engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? Hypotheses 1 H01: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will not have a statistically significant relationship with behavioral student engagement. 86

HR1: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will have a statistically significant relationship with behavioral student engagement. Research Question 2 2. What is the relationship between emotional and cognitive student engagement in science and teacher self-efficacy, complexity level of instructional objectives, student grade level and student achievement? Hypotheses 2 H02: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will not have a statistically significant relationship with emotional and cognitive student engagement. HR2: Individually or in combination, the factors of teacher efficacy, complexity level of instructional objectives, student grade level and student achievement will have a statistically significant relationship with emotional and cognitive student engagement. Discussion of Findings Behavioral Student Engagement The measures of behavioral engagement were used to answer research question 1 and evaluate the corresponding null and alternate hypotheses. Observations of students’ engagement levels were made at intervals. Behavioral engagement levels were used to correlate by gender to the independent variables of grade level, teacher efficacy, complexity of instructional objectives and student achievement. 87

Differences by Gender. Examination of multiple regression results showed significant differences between female and male students. A statistically significant effect on student engagement (active and passive) using the study predictor variables was found for female students, but not for male students. Predicting passive engagement was slightly greater than active engagement for both female and male students. Factors Influencing Female Behavioral Engagement. Analysis of the coefficients for predicting female engagement showed the most significant predictor variable was the science scale score. The standardized beta value was -.27 (sig = .05, sr= -.25) for active engagement and .28 (sig = .04, sr= .27) for passive engagement. Using the multiple regression results for behavioral engagement, the alternate hypothesis was accepted for female students for the predictor variable student achievement. This result indicates that prior success makes a significant impact on female student achievement. Male students are not impacted in the same way as female students and may have different predictors of student engagement. Emotional and Cognitive Student Engagement The measures of emotional and cognitive engagement were used to answer research question 2 and evaluate the corresponding null and alternate hypotheses. Students completed the Science Activity Questionnaire immediately after the science lesson. Composite scores of student engagement levels were calculated and used to correlate by gender to the independent variables of grade level, teacher efficacy, complexity of instructional objectives and student achievement. Preliminary analysis of student engagement levels did not show any significant correlations between any of the predictor variables. This measure of engagement was not 88

related to any of the study’s predictor variables. Therefore, the null hypothesis was accepted. Students’ perceptions of their engagement may be from their perceptions of prior ability more than the actual classroom events. Study findings did not yeild the results expected contributions to student engagement. This does not suggest that teacher efficacy and instructional complexity are not important to student achievement. However it does suggest that the measures used in this study do not adequately measure the interactions of elements in the Student Engagement Core Model: Relationships, relevance and competence. Implications Student engagement levels were found to be most significantly influenced by the students’ past success in science. Research suggests that the elements of the SEC model (Figure 1) teacher efficacy and content play major roles for school improvement. However, since their impact was found to be less influential for the development of student engagement, these interventions may not as significantly improve student engagement. Taking into consideration the Self-Determination Model of Motivational Development (Figure 3), and the Path Analysis of Skill Performance (Figure 4) students’ ability to learn and become engaged in school may rely heavily on their perceived level of competence (Schunk, 1984; Skinner & Chi, 2012). Difference between female and male students in this study underscore findings that the development of competence is different by gender (Archer et al., 2013; Archer & Tomei, 2014; DeWitt, Archer, & Osborne, 2014). The gender differences found in student engagement in this study indicate that the mechanism for the development of selfefficacy is different for females and males. Research has found that experiences during 89

students’ elementary school years have a formative and lasting influence on students’ science beliefs, attitudes, and future career choices (Blatchford, 1992; Jarvis & Pell, 2005; Musgrove & Batcock, 1969). Understanding how female students develop selfefficacy may help redefine society’s traditional gender identification roles. Increasing their self-efficacy during the critical identity forming developmental years may lead to a greater persistence in science education. The cloud of uncertainty in developing student engagement that is highlighted by this study suggest that the focus of improving student engagement improvement be not on the elements of the Student engagement Core model individually, but on how those elements interact. Building relationships, demonstrating relevance of content and building both student and teacher competence may have a greater impact on student engagement than the content, student and teacher individually. Recommendations for Practice This study has implications for leaders to develop school improvement plans that enhance student engagement. Additionally, district policies offer opportunities for revision to support student engagement in science. Gaps between policy and practice are examined as potential areas for examination by school leaders. Implications for Educational Policy The rational for this study was that students who are engaged in school have higher levels of academic achievement (Connell & Wellborn, 1991; Finn & Rock, 1997; Fredricks, Blumenfeld, & Paris, 2004; Lau, Roeser, & Kupermintz, 2002; McPartland, 1994; Sinclair & Christenson, 1998). However, it is unclear what factors, and to what extent, student engagement can be modified (Fredricks, Blumenfeld, & Paris, 2004). 90

Federal policy of NCLB and Race to the Top initiatives may have inadvertently created school climates that push rigor over student readiness. The focus on academic testing and test preparation may frustrate students who do not have the academic readiness for those tasks. A consequence of high stakes testing may be negatively impacting the self-competence of struggling students. Analysis of assessment protocols and how they are rolled out to students and families is important. Sending a clear message that assessments are a snapshot in time, and that they do not define future opportunities. Teacher evaluation has placed student academic performance on high stakes testing as an indicator of teacher performance (Danielson, 2007; System for Educator Evaluation and Development, 2014; The New Teacher Project, 2012). Many problems with the reliability of using student achievement data to determine teacher efficacy have been found. While a more effective teacher may improve student achievement outcomes, the student demographic variables have a much greater impact on student achievement (Darling-Hammond, Amrein-Beardsley, Haertel, & Rothstein, 2011). The results of this study suggest that high stakes testing may influence how teachers teach by placing more emphasis on these tests and unintentionally reinforce their value as the ultimate measure of student success and subject are competency. Implications for School Leaders Understanding how adult actions influence student self-efficacy in science seems to be rooted in how we raise our children. In a recent TED talk, Rashma Saujani described how we raise our girls to be perfect and our boys to be brave (Saujani, 2016, February). This underlying difference may explain why female students seem to define 91

their competence in math and science by perfection. Female students seem to internalize difficulty in learning as an innate and unchangeable characteristic, while males internalize a mindset of developing ability through effort and practice. Praising female students for being smart may reinforce this idea that ability is a characteristic from birth (Dweck, 2006; Halvorson, 2011, January 27). This study highlights the need for students to be taught that ability is not a character trait. Changing the instructional focus away from summative assessments and towards an academic growth model may encourage struggling learners from giving up on learning. Consideration should be made upon how we grade students. The A through F system that begins in middle school rates students’ academic ability from perfection to failure. Standards based grading may be better at measuring and reinforcing growth in student learning. Instructional practice and school wide initiatives should focus on the interactions of the Student Engagement Core model. Identification of opportunities for student and teachers to interact and build relationships outside the traditional academic setting will help students build trust in teachers when their competence in learning wanes. Additionally, teachers will have more opportunities to see students outside their subject area. This knowledge will help them learn more about their students and help them better design instruction to align to their interests and goals. Structured opportunities to focus on both short term and long-term goal setting will help students understand how learning will apply to their life goals.

92

Finally, professional development for teachers and staff will support school initiatives to move the focus from perfection towards growth. This study recommends that school improvement focus on professional development that ensures “just right:” 1. Instruction: Performance tasks that allow students to grow in learning and is matched to be at a reachable level. 2. Evaluation: Assessments should be measures of growth. Expand the use of standards based grading and report cards at higher grade levels to measure performance mastery and change the focus to growth. 3. Feedback: Reflection on student learning should highlight growth and not measurements against perfection. Praise on persistence and determination in problem solving should be the focus. 4. Intervention: Support students when frustrated and provide them with direct emotional and academic support in order to prevent maladaptive coping strategies. 5. Social Support: Family and community messages should focus on how to support students’ frustration in learning. Changing praise messages away from measures of perfection and focus on improvement based upon persistence and determination. Questions for Future Research The study of student engagement in science has been shown to be a critical component to improving student achievement and the development of school reform initiatives. The impact of student disengagement is becoming clearer. However, the understanding of what factors influence student engagement and the development of 93

intervention strategies remains unclear. The following are possible research areas needed to understand the development and develop intervention strategies for science student engagement: 1. What is the impact of grades in determining student self-efficacy? 2. How do students perceive feedback on their learning and what is the impact on student self-efficacy? 3. How do students’ perceptions of their competence in science change over time? 4. What factors influence students’ identification of competence with science content? 5. How do differences in persistence and determination vary by school content area develop? 6. What is the impact of school district building transitions on student engagement and self-efficacy in science? 7. Are there differences in the way students are encouraged and supported in their science education by educators, families and the community? Summary The findings of this study further the understanding of the development of student engagement. Results showed that behavioral engagement of female students was correlated significantly to science achievement tests. Male student results showed no significant correlations to levels of engagement. Analysis of survey results showed no significant relationships between predictor variable and composite measures of cognitive and emotional engagement. 94

Student engagement is rooted in students’ perception of their competence to learn within the context of the instructional core. Male students seem to have a more fluid perception of their ability while females define their ability by past success. Supporting students in developing persistence and academic risk taking will increase resilience and determination to learn. These students will more easily engage in difficult academic tasks and continue into higher-level science subjects over their academic careers.

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APPENDIX

96

A. IRB Protocol and Consent Approval Letter

97

B. Permission for Use (Figure 1)

98

C. Permission for Use (Figure 2)

99

D. Permission for Use (Figure 3)

100

E. Permission for Use (Figure 4)

101

F. Webb’s Depth of Knowledge (DOK) Levels

102

103

G. Strategic School Profile for Quinnipiac Middle School

104

105

106

107

108

109

H. Informed Consent for Study Participation

110

111

112

113

114

115

I. BOSS Observation Instrument

116

J. SAQ Instrument Usage Agreement

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K. SAQ Instrument PROCTOR INSTRUCTIONS Location Bring the students to the Media Center or Computer Lab to complete the survey portion of the study. The Media Center teacher will redirect you to the Computer Lab if the Media Center is unavailable. Four computers will be available for survey administration. Administration should take less than fifteen minutes. Upon completion of the survey, give each student a late pass to proceed to their next class. If at any point any student(s) wish(es) to remove themselves from the study, give them a pass to return to class without question. While it may seem helpful to try to convince a student to continue, it may influence the results and jeopardize the validity of the study. Survey Set Up The computers should be on prior to arriving to the survey administration location (media center or computer lab). Direct students to click on the bookmark on the desktop named “SAQ Survey”. This will launch the survey instrument. Survey Completion Using the Study packet, locate the student list. READ: Click on the Science Activity Questionnaire icon on the computer desktop. I will give each of you individual Student Number. Once all students have correctly entered their student numbers and they have been checked for accuracy, locate the Session ID and Teacher Number in the Study Packet. READ: Find the field labeled Session ID. Enter the number “______”. Once all students have correctly entered the Session ID and they have been checked for accuracy, locate the Teacher Number in the Study Packet. READ: Find the field labeled Teacher Number. Enter the number “______”. Check each students’ entry prior to proceeding. READ: We will now complete the first session of the survey. Read the directions as I read it aloud. Students have a lot of different thoughts and feelings while they are doing their science work. We want to know how true each of these things below was for you. 118

Remember there are no right and wrong answers. Circle the answer that best describes your feelings. Be sure to circle only one answer for each sentence. If the sentence describes you a lot, select VERY TRUE. If the sentence is pretty close to how you felt but not exactly, select SOMEWHAT TRUE. If the sentence describes you only a little, select A LITTLE TRUE. Select NOT AL ALL TRUE, if the sentence does not describe you. Once you finish Part one stop. Wait for students to complete part 1. Check to ensure all questions are completed. If students ask for clarification, instruct them to complete the question that best fits them in any context they relate the question to within todays lesson. READ: These sentences describe different reasons for doing schoolwork. Different kids have different reasons. We want to know how true each of their reasons was for why you did your science work. If the sentence describes you a lot, select A LOT LIKE ME. If the sentence does not describe you at all, select NOT AT ALL LIKE ME. Once you finish Part two stop. Wait for students to complete part 2. Check to ensure all questions are completed. READ: There are many different ways students do their work. We want to know how much each of these things are like what you did in science. Select A LOT LIKE ME if the sentence is very much like what you did If the sentence is sort of like what you did, select A LITTLE LIKE ME. Select NOT AT ALL LIKE ME if the sentence does not describe what you did. Once you finish Part three stop, do not click submit. Wait for students to complete part 3. Check to ensure all questions are completed. Once all question have been completed: READ: We are now finished with the survey. Click on the SUBMIT button at the bottom. Thank you for participating and helping educators learn more about how students learn science. I will give you each a pass to continue to your next class.

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