This study examines the proportional reasoning strategies used by physics Ph.D. candidates in real-time by applying Carlson et al.’s (2002) mental action framework to the Ph.D. candidates’ cognitive processes. The study conducted eight individual think-aloud interviews during which each participant solved eight separate proportional reasoning tasks. The interviews were recorded and segmented into a total of 64 clips for analysis, and each clip was analyzed using Carlson’s framework. The research team found that while Carlson’s framework is useful, it may not fully capture the nuances of experts’ proportional reasoning strategies, highlighting the need for a more comprehensive approach to the study of covariational reasoning in physics education research.

Graduate students and graduate degree holders play a central role in the growth of science and technology for both institutions and the country. They serve as researchers pursuing advances in science and educators preparing the next generation of scientists. Historically STEM graduate programs have had a low retention rate, potentially causing a high cost for both those leaving the programs and for society at large. In this talk we look at the past two decades of the American Institue of Physics’ annual rosters with a focus on the number of graduate students in programs and the graduation rates for these students. By observing the trends over the past two decades, we can make general observation about the current state of graduate education in the United States. We find that while over the past 20 years the general trend has been towards an increase in graduate students in programs across the country as well as annual degrees earned. We find that there is variation in the retention rates of different programs. Our results can better inform prospective graduate students and faculty about current trends in the field of education and potentially inspire change. 

Generative Artificial Intelligence (gen-AI) is rapidly becoming more integrated into today's classrooms in all ranges of education. In higher education, Gen-AI is often seen as a resource for students, aiding them in drafting outlines, solving simple mathematical problems, or even decoding or constructing code. In this paper, we analyze essay-based interviews (N=6) from an upper-division computational physics course, in which physics majors addressed their views and attitudes towards Gen-AI and how it affects their learning. We analyzed the concepts of creativity and gen-AI using the Four C Model, a framework encompassing four types of creativity. Our analysis of the data involved coding and characterizing students' definitions of creativity and generative AI. Our findings revealed two main observations: first, students conceptualized their creativity primarily within mini-c and little-c; second, students perceived gen-AI as a resource and learning tool but expressed skepticism regarding its accuracy and creativity.

Physics graduate programs require students to pass a candidacy exam to change their status from a graduate student to a Ph.D. Candidate. The format and scope of candidacy exams differ from one department to the next. Literature on candidacy exams' purpose, format, and efficacy is scarce. Our study seeks to contribute to the literature on candidacy exams in physics.  For that purpose, we developed a framework that will help to investigate graduate students' perspectives of oral candidacy exams.  Our conceptual framework consists of 3 interconnected parts: physics identity, graduate student identity, and doctoral student development. Coded interview excerpts and initial data analysis results will be presented. 

Idealized models are a natural part of physics research and education. In upper-division physics courses, which tend to be more abstract and mathematically advanced than the introductory courses, such simplified toy models often serve as pedagogical tools to illustrate concepts and calculations. In the topic of statistical mechanics, we have studied the challenges faced by small problem-solving groups of upper-division students. Our findings indicate that students struggle to recognize the underlying structure of commonly used toy models. Students faced various pitfalls as they tended to rely on surface features of the tasks, in combination with loosely connected ideas about key concepts. Based on our findings, we discuss recommendations for teachers in statistical mechanics and other advanced courses that resort to simplified models to explain complex ideas.

Undergraduate physics students are often taught and retaught mathematical methods in multiple courses in multiple ways, but may still not be prepared to transfer those methods to new contexts. Here, we sought to develop a tutorial for students to learn how physicists approach the strategy of “Separation of Variables” for solving second-order partial differential equations. Students create and revise their own list of steps for the Separation of Variables procedure while working through the tutorial. We produced versions with different orderings of a Quantum Mechanics, Electricity and Magnetism, and Heat Equation example, all with a gradual reduction of scaffolding between these examples. We intend that these different versions of this tutorial can be used in a Math Methods course, or an upper-division E&M or Quantum Mechanics course. We elicited feedback from undergraduate students, graduate students, and Math and Physics faculty when producing these tutorials, and piloted it in an undergraduate E&M class. One unique aim of this tutorial is to include a focused amount of content without requiring extensive background knowledge, in contrast to some existing tutorial/explanation sections of textbooks. We hope that this tutorial and surrounding research will help us better support students who will use mathematical methods in physics courses.

Collaborative learning with peers can lead to students learning from each other and solving physics problems correctly not only in situations in which one student knows how to solve the problems but also when none of the students can solve the problems alone. In the latter situation, students are co-constructing knowledge that helps them solve the problems, while in the former, one student helps the other construct knowledge. In this study, we investigated student learning measured by student performance on a validated quantum mechanics survey and frequencies of construction and co-construction of knowledge when students first worked individually after lecture-based instruction in relevant concepts and then worked with peers during class without receiving any feedback from the course instructor. We find that the construction of knowledge consistently occurred at a high rate during peer collaboration. However, rates of co-construction were more varied.