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The capability of large language models (LLMs) to generate, debug, and explain code has sparked the interest of researchers and educators in undergraduate programming, with many anticipating their transformative potential in programming education. However, decisions about why and how to use LLMs in programming education may involve more than just the assessment of an LLM's technical capabilities. Using the social shaping of technology theory as a guiding framework, our study explores how students' social perceptions influence their own LLM usage. We then examine the correlation of self-reported LLM usage with students' self-efficacy and midterm performances in an undergraduate programming course. Triangulating data from an anonymous end-of-course student survey (n = 158), a mid-course self-efficacy survey (n=158), student interviews (n = 10), self-reported LLM usage on homework, and midterm performances, we discovered that students' use of LLMs was associated with their expectations for their future careers and their perceptions of peer usage. Additionally, early self-reported LLM usage in our context correlated with lower self-efficacy and lower midterm scores, while students' perceived over-reliance on LLMs, rather than their usage itself, correlated with decreased self-efficacy later in the course., Comment: Accepted to the ACM Conference on International Computing Education Research V.1 (ICER '24 Vol. 1)

The Robotics Major at the University of Michigan was successfully launched in the 2022-23 academic year as an innovative step forward to better serve students, our communities, and our society. Building on our guiding principle of "Robotics with Respect" and our larger Robotics Pathways model, the Michigan Robotics Major was designed to define robotics as a true academic discipline with both equity and excellence as our highest priorities. Understanding that talent is equally distributed but opportunity is not, the Michigan Robotics Major has embraced an adaptable curriculum that is accessible through a diversity of student pathways and enables successful and sustained career-long participation in robotics, AI, and automation professions. The results after our planning efforts (2019-22) and first academic year (2022-23) have been highly encouraging: more than 100 students declared Robotics as their major, completion of the Robotics major by our first two graduates, soaring enrollments in our Robotics classes, thriving partnerships with Historically Black Colleges and Universities. This document provides our original curricular proposal for the Robotics Undergraduate Program at the University of Michigan, submitted to the Michigan Association of State Universities in April 2022 and approved in June 2022. The dissemination of our program design is in the spirit of continued growth for higher education towards realizing equity and excellence. The most recent version of this document is also available on Google Docs through this link: https://ocj.me/robotics_major, Comment: 49 pages, approximately 25 figures

Undergraduates are unlikely to even consider graduate research in Computer Science if they do not know what Computer Science research is. Many programs aimed at introducing undergraduate to research are structured like graduate research programs, with a small number of undergraduates working with a faculty advisor. Further, females, under-represented minorities, and first generation students may be too intimidated or the idea of research may be too amorphous, so that they miss out on these programs. As a consequence, we lose out on opportunities for greater diversity in CS research. We have started a pilot program in our department where a larger number of students (close to two dozen) work with a single faculty member as part of a research group focused on Machine Learning and related areas. The goal of this program is not to convince students to pursue a research career but rather to enable them to make a more informed decision about what role they would like research to play in their future. In order to evaluate our approach, we elicited student experience via two anonymized exit surveys. Students report that they develop a better understanding of what research in Computer Science is. Their interest in research was increased as was their reported confidence in their ability to do research, although not all students wanted to further pursue computer science research opportunities. Given the reported experience of female students, this program can offer a starting point for greater diversity in CS research.

Computational science includes the use of computer-based modeling and simulation to define and test theories about scientific phenomena. The challenge for educators is to develop techniques for implementing computational science in the classroom. This paper reviews some previous work on the use of simulation alone (without modeling), modeling alone (without simulation), and computer-based modeling and simulation. In addition, it describes Emile, an environment which offers software-realized scaffolding to support student modeling activities. Four figures depict a physics explorer simulation of gravity; a student's simulation created in Emile; and Emile text program components. (Contains 23 references.) (AEF)