Investigating the Predictive Power of Student Characteristics on

University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2016 Investigating the Predictive Power of Student Characteristics on Success in Studio-mode, Algebra-based Introductory Physics Courses Jarrad Pond University of Central Florida Part of the Physics Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS For more information, please contact STARS@ucf.edu STARS Citation Pond, Jarrad, "Investigating the Predictive Power of Student Characteristics on Success in Studio-mode, Algebra-based Introductory Physics Courses" (2016) Electronic Theses and Dissertations, 2004-2019 5098 https://stars.library.ucf.edu/etd/5098 INVESTIGATING THE PREDICTIVE POWER OF STUDENT CHARACTERSITICS ON SUCCESS IN STUDIO-MODE, ALGEBRA-BASED INTRODUCTORY PHYSICS COURSES by JARRAD WILLIAM THOMAS POND B.S University of Central Florida, 2009 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Physics in the College of Sciences at the University of Central Florida Orlando, Florida Summer Term 2016 Major Professors: Talat S Rahman, Jacquelyn J Chini © 2016 Jarrad William Thomas Pond ii ABSTRACT As part of a project to explore the differential success of similar implementations of the studio-mode of physics instruction, the objective of this work is to investigate the characteristics of students enrolled in algebra-based, studio-mode introductory physics courses at various universities in order to evaluate what effects these characteristics have on different measures of student success, such as gains in conceptual knowledge, shifts to more favorable attitudes toward physics, and final course grades In my analysis, I explore the strategic self-regulatory, motivational, and demographic characteristics of students in algebra-based, studio-mode physics courses at three universities: the University of Central Florida (UCF), Georgia State University (GSU), and George Washington University (GW) Each of these institutions possesses varying student populations and differing levels of success in their studio-mode physics courses, as measured by students’ overall average conceptual learning gains In order to collect information about the students at each institution, I compiled questions from several existing questionnaires designed to measure student characteristics such as study strategies and motivations for learning physics, and organization of scientific knowledge I also gathered student demographic information This compiled survey, named the Student Characteristics Survey (SCS) was given at all three institutions Using similar information collected from students, other studies (J A Chen, 2012; Nelson, Shell, Husman, Fishman, & Soh, 2015; Schwinger, Steinmayr, & Spinath, 2012; Shell & Husman, 2008; Shell & Soh, 2013; Tuominen-Soini, Salmela-Aro, & Niemivirta, 2011; Vansteenkiste, Soenens, Sierens, Luyckx, & Lens, 2009) have identified distinct learning profiles across varying student populations Using a iii person-centered approach, I used model-based cluster analysis methods (Gan, Ma, & Wu, 2007) to organize students into distinct groups From this analysis, I identified five distinct learning profiles in the population of physics students, similar to those found in previous research In addition, student outcome information was gathered from both UCF and GSU Conceptual inventory responses were gathered at both institutions, and attitudinal survey results and course grades were gathered at UCF No student outcome data was gathered at GW; thus, GW is represented in analyses involving information compiled solely from the SCS, but GW is not represented in analyses involving student outcome information Then, I use Automatic Linear Modeling, an application of multiple linear regression modeling (IBM, 2012, 2013), to identify which demographic variables (including the identified learning profiles) are the most influential in predicting student outcomes, such as scores on the Force Concept Inventory (FCI), the Conceptual Survey of Electricity and Magnetism (CSEM), and the Colorado Learning Attitudes about Sciences Survey (CLASS), both pre- and postinstruction Modeling is conducted on the entire available dataset as a whole and is also conducted with the data disaggregated by institution in order to identify any differential effects that student characteristics may have at predicting student success at the different institutions In addition, instructors teaching algebra-based, studio-mode introductory physics courses are interviewed about what makes students successful in order to better understand what instructors perceive is important for students to excel in their physics courses Furthermore, student survey takers were interviewed to help verify their study strategies and motivations as measured by the SCS iv The above analysis provides evidence that, on average, gaps in student understanding exist based on several demographic characteristics, such a gender, ethnicity, high school physics experience, and SAT Math score, and these results are generally consistent with those found in the literature Disaggregation by institution reveals that differential effects from demographic variables exist; thus, similar groups of students at separate institutions attain different student outcomes Overall, this is an undesirable observation, as the physics education research community strives to reduce such inequity in physics classrooms; however, identification of specific inequities and gaps in learning will help to inform further research investigations Research should continue in the form of in-depth investigations into how individual instructors teach algebra-based studio-mode introductory physics courses, focusing on instructors’ approaches to the studio-mode of instruction and uses of active learning techniques Also, investigation of instructor awareness of demographic-driven gaps in student understanding would give insight into if and how instructors may be attempting to better understand the needs of different students In addition, where a wide range of demographic data are available, I encourage institutions to conduct similar analyses as those presented here in order to identify any gaps in student understanding and place them in their institutional contexts for comparisons to other universities Furthermore, as a result of my work, I find the identified learning profiles to have a significant association with students’ attitudes toward physics, as measured by the CLASS questionnaire, both pre- and post-instruction This relationship between learning profile and CLASS Pre-score is one that can help give instructors practical insight into students’ study strategies and motivations at the very beginning of the physics course By possessing knowledge v of which students and not possess adaptive learning strategies early on, instructors can better optimize initial student groups by considering results of student outcome measures, adjust lesson plans, and assess students’ needs accordingly vi I dedicate this dissertation to my wife and best friend, Joanie Her friendship, love, support, and comfort were integral to the completion of this work vii ACKNOWLEDGMENTS There are a fair number of people I would like to acknowledge First, I want to thank Dr Jacquelyn Chini for her support, guidance, and understanding Throughout the personal challenges I have faced while completing my doctoral work, Dr Chini made it her goal to prepare me for my career in physics education I want to also thank Dr Talat Rahman for her support and for being there for me in times of personal crisis; I may very well not be in physics education – my preferred field – if not for her Next, I want to thank my collaborators: Dr Joshua Von Korff and Dr Brian Thoms at Georgia State University, Dr Gerald Feldman at the George Washington University, and Dr Jon Gaffney at Eastern Kentucky University These individuals provided extremely useful feedback that helped to guide this work Also, I want to acknowledge my fellow physics education research graduate students at UCF, Matthew Wilcox, Westley James, and Brian Zamarippa Roman, with whom sharing ideas and laughs have made my graduate experience that much better Lastly, I want to acknowledge my mother, Jodi, for her continued support, despite the trials and tribulations our family has faced over the years Without her encouragement, I would certainly not have gotten this far in my career In addition, I would also like to acknowledge National Science Foundation grants DUE 1347510, 1347515, 1347527, and 1246024 for the financial support Thank you, everyone! viii TABLE OF CONTENTS LIST OF FIGURES xv LIST OF TABLES xvi LIST OF ACRONYMS AND ABBREVIATIONS xxii CHAPTER ONE: INTRODUCTION Motivation Scope of Research Research Goals and Research Questions Overview of Methodologies Organization of Dissertation CHAPTER TWO: LITERATURE REVIEW Active Learning The Studio Mode of Physics Instruction 12 Workshop Physics 12 Studio Physics at Rensselaer Polytechnic Institute 14 Student-Centered Active Learning Environment for Undergraduate Programs 19 ix Kohl, P B., & Kuo, V H (2012) Chronicling a successful secondary implementation of Studio Physics American Journal of Physics, 80(9), 832-839 doi:doi:http://dx.doi.org/10.1119/1.4712305 Kolb, D (1984) Experiential learning: experience as the source of learning and development Koriat, A., & Nussinson, R (2009) Attributing study effort to data-driven and goal-driven effects: Implications for metacognitive judgments Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(5), 1338-1343 doi:10.1037/a0016374 Kortemeyer, G (2007) Correlations between student discussion behavior, attitudes, and learning Physical Review Special Topics - Physics Education Research, 3(1), 010101 Kost, L E., Pollock, S J., & Finkelstein, N D (2009) Characterizing the gender gap in introductory physics Physical Review Special Topics - Physics Education Research, 5(1), 010101 Kostons, D., van Gog, T., & Paas, F (2012) Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning Learning and Instruction, 22(2), 121-132 doi:http://dx.doi.org/10.1016/j.learninstruc.2011.08.004 Krapp, A (1999) Interest, motivation and learning: An educational-psychological perspective European Journal of Psychology of Education, 14(1), 23-40 doi:10.1007/bf03173109 Kuprys, A., & Kugelevičius, J (2009) Possibilities of using liquefied oil gas in transport Transport, 24(1), 48-53 Landis, J R., & Koch, G G (1977) The Measurement of Observer Agreement for Categorical Data, 159 Larkin, J (1981) Cognition of learning physics American Journal of Physics, 49(6), 534-541 365 Lasry, N., Charles, E., & Whittaker, C (2014) When teacher-centered instructors are assigned to student-centered classrooms Physical Review Special Topics - Physics Education Research, 10(1), 010116 Laverty, J T., Cooper, M M., & Caballero, M D (2015, July 29-30) Developing the Next Generation of Physics Assessments Paper presented at the Physics Education Research Conference 2015, College Park, MD Laws, P W (1991) CALCULUS-BASED PHYSICS WITHOUT LECTURES Physics Today, 44(12), 24-31 Laws, P W (1997) Millikan Lecture 1996: Promoting active learning based on physics education research in introductory physics courses American Journal of Physics, 65(1), 14-21 doi:doi:http://dx.doi.org/10.1119/1.18496 Laws, P W., Rosborough, P J., & Poodry, F J (1999) Women's responses to an activity-based introductory physics program American Journal of Physics(7), 32 Lindsey, B A., & Nagel, M L (2015) Do students know what they know? 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