The CS Education Research (CER) Lab explores ways to improve outcomes for all students interested in studying computing. Our recent efforts include proposing and evaluating new pedagogies, using machine learning to identify struggling students, designing assessments to measure learning, uncovering the source of student struggles, and creating new course structures to improve sense of community.
Project Lead: Anshul Shah
My work aims to develop pedagogical approaches to address the academia-industry gap. Specifically, I aim to identify and address students’ struggles related to program comprehension, code quality, and use of LLM tools while working with large code bases.
Project Lead: Ismael Villegas Molina
The focus of my research is to enhance computer science education for Latine students. I am currently working on this through culturally relevant methodologies. My ongoing projects involve 1) developing and assessing culturally relevant materials for introductory CS courses, incorporating diverse Latine cultures (e.g., Mexican, Peruvian, Guatemalan, Brazilian, etc.) to a racially/ethnically diverse classroom, and 2) gathering insights from CS professors at Minority Serving Institutions on their culturally relevant teaching strategies. Previously, I conducted a comprehensive literature review on efforts to support Latine students in computer science and explored the use of Generative AI for crafting culturally relevant materials, assessing its effectiveness in reflecting Latine cultural elements. Additionally, I implemented bilingual Spanish-English computer science instruction for high school students, analyzing its impact on learning outcomes.
Project Lead: Annapurna Vadaparty
Large Language Models (LLMs) like ChatGPT and GitHub Copilot are reshaping computing education, especially in introductory programming courses (CS1). These tools solve traditional assignments and exams, challenging what is taught and assessed. As professional engineers adopt LLMs, preparing students to use them is essential. To address this, we developed an LLM-integrated CS1 course (CS1-LLM) for UCSD's introductory CS course. This course shifts focus from syntax mastery and coding from scratch to skills like explaining and testing code and breaking down problems into prompts for LLMs. We seek to understand how students can best learn in the new LLM-assisted learning environments, and find out how to best support students with computing fundamentals of the LLM era.
Project Lead: Emma Hogan
The computing education lab has been engaged in research on improving computing higher education in prison since 2022. To date, this project has involved teaching an introductory computing course at Richard J. Donovan prison every year, fulfilling a technology requirement for students pursuing a Bachelor’s degree through UC Irvine’s LIFTED program. So far, we have taught 72 incarcerated college students over three years in these courses, and have published research in SIGCSE and ITiCSE on strategies for improving this course for the prison setting including identifying meaningful computing contexts for incarcerated adults, and identifying fears and changes in confidence amongst this population.
Moving forward, the lab has plans to begin expanding the computing courses offered in the prison starting in Winter 2025, strengthening the partnership between the UC Irvine LIFTED program and the UCSD CSE department. We also plan to expand our research through examining pathways for formerly-incarcerated individuals with training in computing skills into the computing industry and academia.
Undergraduate students are welcome to apply to join the CER lab through the Early Research Scholars Program.
Graduate students are welcome to apply to join the CER lab through the UCSD Computer Science & Engineering Department Graduate Admissions.