The underrepresentation of women in physics has been well documented and a source of concern for both policy makers and educators. My dissertation focuses on understanding the role self-efficacy plays in retaining students, particularly women, in introductory physics. I use an explanatory mixed methods approach to first investigate quantitatively the influence of self-efficacy in predicting success and then to qualitatively explore the development of self-efficacy. In the initial quantitative studies, I explore the utility of self-efficacy in predicting the success of introductory physics students, both women and men. Results indicate that self-efficacy is a significant predictor of success for all students. I then disaggregate the data to examine how self-efficacy develops differently for women and men in the introductory physics course. Results show women rely on different sources of self-efficacy than do men, and that a particular instructional environment, Modeling Instruction, has a positive impact on these sources of self-efficacy. In the qualitative phase of the project, this dissertation focuses on the development of self-efficacy. Using the qualitative tool of microanalysis, I introduce a methodology for understanding how self-efficacy develops moment-by-moment using the lens of self-efficacy opportunities. I then use the characterizations of self-efficacy opportunities to focus on a particular course environment and to identify and describe a mechanism by which Modeling Instruction impacts student self-efficacy. Results indicate that the emphasizing the development and deployment of models affords opportunities to impact self-efficacy. The findings of this dissertation indicate that introducing key elements into the classroom, such as cooperative group work, model development and deployment, and interaction with the instructor, create a mechanism by which instructors can impact the self-efficacy of their students. Results from this study indicate that creating a model to impact the retention rates of women in physics should include attending to self-efficacy and designing activities in the classroom that create self-efficacy opportunities.

Representations (such as pictures, diagrams, word descriptions, equations, etc.) are critical tools for learning, problem solving, and communicating in science, particularly in physics where multiple representations often serve as intermediate steps, a means to evaluate a solution, and highlight different aspects a physical phenomenon. This dissertation explores the representation choices made by students in the University Modeling Instruction (MI) courses on problems from across introductory physics content. Modeling Instruction is a two-semester introductory, calculus-based physics sequence that was designed to guide students through the process of building, testing, applying, and refining models. As a part of this modeling cycle, students have explicit instruction and practice in building, evaluating, and coordinating representations in introductory physics. Since I am particularly interested in representations across all of introductory physics, this work was situated in the second semester of MI. To address students' representation choices, the Problem Solving and Representation Use Survey (PSRUS) was developed as modified card sort survey, which asked students to simply list the representations that they would use on 25 physics questions from across introductory physics. Using non-parametric statistical tests (Mann-Whitney-Wilcox, Wilcoxon-Ranked Sign, and Cliff's Delta), I compare the number and variety of representations that students choose. Initially, students who took the first semester of MI use significantly more representations in their problem solving when compared to those who did not; however, there are significant gains in the number of representations that these students choose over the semester across the introductory physics content. After significant changes to the second semester MI curriculum, the difference between these two groups disappears, with both groups increasing their representation choices when compared to the previous semester. Using network analysis to compare students' concurrent representation choices, I also show that students use a consistent set of representations on mechanics problems; whereas, they choose a wider variety on electricity and magnetism (EM) problems. In both mechanics and EM, pictures serve as an important connecting representation between the others. I use these results to make suggestions for instructors, curriculum developers, and physics education researchers.

The purpose of this study was to investigate the effect of modeling instruction over traditionally designed physics instruction on students&rsquo / understanding of projectile motion concepts and their attitudes towards physics. In addition, the effects of gender difference on their understanding of projectile motion concepts and attitudes towards physics were explored. Furthermore, students&rsquo / views on the nature of science were searched. The subjects of this study included 88 tenth grade students of four classes instructed by two teachers in a private high school. One of two classes of each teacher was randomly assigned to experimental group and other classes formed control group. The modeling instruction was applied in the experimental group to teach the topic of projectile motion, it was taught with traditionally designed physics instruction in control group. Projectile Motion Concept Test, Attitude Scale towards Physics, Science Process Skill Test, and Views on Science-Technology-Society test were administered to both groups. In addition, student interviews and classroom observations were conducted. The hypotheses of the research were tested by using ANCOVA and two-way ANOVA. The results revealed that the mean score of experimental group students&rsquo / on both concept test and attitude scale was significantly higher than the mean score of control group students. Furthermore, gender was not a significant factor affecting the concept acquisition related to projectile motion and students&rsquo / attitudes towards physics. However, science process skill was determined as a strong predictor in conceptual understanding. Lastly, experimental group students had more realistic views on some basic tenets of nature of science.

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This dissertation presents a series of neuroimaging investigations and achievements that strive to deepen and broaden our understanding of human problem solving and physics learning. Neuroscience conceives of dynamic relationships between behavior, experience, and brain structure and function, but how neural changes enable human learning across classroom instruction remains an open question. At the same time, physics is a challenging area of study in which introductory students regularly struggle to achieve success across university instruction. Research and initiatives in neuroeducation promise a new understanding into the interactions between biology and education, including the neural mechanisms of learning and development. These insights may be particularly useful in understanding how students learn, which is crucial for helping them succeed. Towards this end, we utilize methods in functional magnetic resonance imaging (fMRI), as informed by education theory, research, and practice, to investigate the neural mechanisms of problem solving and learning in students across semester-long University-level introductory physics learning environments. In the first study, we review and synthesize the neuroimaging problem solving literature and perform quantitative coordinate-based meta-analysis on 280 problem solving experiments to characterize the common and dissociable brain networks that underlie human problem solving across different representational contexts. Then, we describe the Understanding the Neural Mechanisms of Physics Learning project, which was designed to study functional brain changes associated with learning and problem solving in undergraduate physics students before and after a semester of introductory physics instruction. We present the development, facilitation, and data acquisition for this longitudinal data collection project. We then perform a sequence of fMRI analyses of these data and characterize the first-time observations of brain networks underlying physics problem solving in students after university physics instruction. We measure sustained and sequential brain activity and functional connectivity during physics problem solving, test brain-behavior relationships between accuracy, difficulty, strategy, and conceptualization of physics ideas, and describe differences in student physics-related brain function linked with dissociations in conceptual approach. The implications of these results to inform effective instructional practices are discussed. Then, we consider how classroom learning impacts the development of student brain function by examining changes in physics problem solving-related brain activity in students before and after they completed a semester-long Modeling Instruction physics course. Our results provide the first neurobiological evidence that physics learning environments drive
the functional reorganization of large-scale brain networks in physics students. Through this collection of work, we demonstrate how neuroscience studies of learning can be grounded in educational theory and pedagogy, and provide deep insights into the neural mechanisms by which students learn physics.