This is a research-to-practice full paper. Several research studies indicate that women who have opted into a computing career path must regularly contend with negative stereotypes about their technical abilities. These stereotypes are often cited as contributing factors to the underrepresentation of women in computing. To counter these stereotypes and enhance female participation in computer science, numerous interventions have been designed. However, most existing research tends to rely on anecdotal evidence and questionnaires to study these stereotypes. In contrast, our study collected data from over 900 students over a span of eight years and adopted a comprehensive quantitative approach to examine these stereotypes about female students. We utilized pre-class GitHub contribution metrics to evaluate students' programming experience and an array of in-class grading items to measure students' performance. Additionally, we mined the project repositories' git logs to gain insights into students' contributions to team projects. Our investigation began by probing whether there was a notable difference in the technical backgrounds or preparedness between female and male students. The results indicated that males tended to be better prepared. Next, we explored potential disparities in class performance between the two genders. Our findings revealed that males and females each excelled in different areas. We were also interested in discerning if female and male students contributed equally to team projects; our analysis affirmed that the contributions were comparable between the two groups. If allowed to choose their teammates, we examined whether they showed a preference for single-gender teams or mixed-gender teams. Our conclusions indicated no marked preference. This paper aims to augment the body of research on computing education by assisting educators in gaining a better understanding of female students in the class. Moreover, it tests the stereotypes by comparing them with empirical results.

This research paper describes an interactive system using large language models and prompt engineering to generate rubrics. Rubrics have long been employed to ensure a grading system that is both equitable and consistent. In practice, generating rubrics could be challenging for instructors for many reasons (e.g., a tight course schedule, limited resources, and varying materials for different projects in the same course), which urges the need to generate the rubric automatically. To the best of our knowledge, little research has been performed on generating rubrics. In this work, we present a novel system based on Large Language Models (LLMs) and Prompt Engineering to help instructors generate rubric items interactively based on course materials, as well as assess the student's work using these rubrics to give timely feedback automatically. In this system, we applied several LLMs (e.g., GPT4, Llama, Falcon, and Hermes) to generate both rubric and feedback using this process: 1) a set of text chunks are initially generated from the textual materials (these textual materials may from various sources), then LDA (Latent Dirichlet Allocation) is applied to extract a set of keywords from the preprocessed text chunks for rubric generation; 2) a web page was designed to let the instructor choose if the keywords from the set are adequate as rubric words; 3) the rubric items are generated by LLMs from the rubric words. In our experiments, a total number of 1017 documents (including the syllabus, the course website, the requirement of projects, the students' works, and the instructors' feedback) were used to build the corpus to generate the rubric-related keywords. Three users (including one instructor and two teaching assistants) participated in generating the rubric interactively using the webpage. The results of experiments show that the interactively generated rubrics from the LLM-instructor system can achieve a level similar to manually created rubrics. We utilized different prompts to let the LLMs generate feedback for the student's work, based on the generated rubrics. Our study shows that generating automatic rubrics and feedback for student project reports is feasible, yet it also identifies significant challenges that future research needs to address.

In modern software engineering education, team formation is crucial for mimicking real-world collaborative scenarios and boosting project-based learning outcomes. This paper introduces a simple, innovative, and universally adaptable method for forming student teams within a software engineering class. We utilize publicly available pre-class GitHub metrics as our input variables (e.g., number of commits, pull requests, code size, etc.). For team formation, the constrained k-means algorithm is employed. This algorithm embraces domain-specific constraints, ensuring the resulting teams not only resonate with the inherent data clusters but also meet educational requirements. Preliminary results suggest that our methodology yields teams with a harmonious blend of skills, experiences, and collaborative potentials, thereby setting the stage for enhanced project success and enriched learning experiences. Quantitative analyses show that teams formed via our approach outperform both randomly assembled teams and student self-selected teams concerning project grades. Moreover, teams created using our method also display a reduced standard deviation in grades, suggesting a more consistent performance across the board.