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Investigating the Predictive Power of Student Characteristics on Success in Studio-mode, Algebra-based Introductory Physics Courses

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Date Issued:
2016
Abstract/Description:
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 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 post-instruction. 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.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 of which students do and do 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.
Title: Investigating the Predictive Power of Student Characteristics on Success in Studio-mode, Algebra-based Introductory Physics Courses.
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Name(s): Pond, Jarrad, Author
Rahman, Talat, Committee Chair
Chini, Jacquelyn, Committee CoChair
Mucciolo, Eduardo, Committee Member
Butler, Malcolm, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2016
Publisher: University of Central Florida
Language(s): English
Abstract/Description: 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 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 post-instruction. 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.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 of which students do and do 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.
Identifier: CFE0006376 (IID), ucf:51515 (fedora)
Note(s): 2016-08-01
Ph.D.
Sciences, Physics
Doctoral
This record was generated from author submitted information.
Subject(s): Self Regulation -- Studio Physics -- Cluster Analysis -- Multiple Linear Regression
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0006376
Restrictions on Access: public 2016-08-15
Host Institution: UCF

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