Bridget Agyare	Bridget is a new PhD student as of Fall 2024, and she is interested in broadening participation in computing. During her PhD, she aims to investigate systemic barriers to success in higher education and the field of computer science for students from historically underrepresented groups. Advisor: Colleen Lewis
Aryan Arora	Aryan joined as a Masters student in Fall 2025. He is interested in the development of learning tools for CS Education, his current research focus includes developing feedback based grading technologies. Advisor: Elsa Gunter
Katherine Braught	Katherine is a PhD student focused on CS theory education. She is interested in how students learn algorithms and how to broaden participation in high level courses in computing. She also studies how to improve student motivation to learn advanced theory topics. Advisor: Jeff Erickson
Heather Broome	Heather joined the PhD program in Fall 2024, focusing her research on responsible AI development. She is interested in creating transparent machine learning tools that prioritize societal well-being, with applications that support equitable and accessible learning environments. Advisor: Chad Lane
Hongxuan Chen	Hongxuan joined as a PhD student in fall 2021, and he is interested in how students study CS and understand difficult material. Hongxuan has evaluated the impact of multiple course policies and is now investigating how students learn advanced graph algorithm concepts. Advisor: Geoffrey Herman & Jeff Erickson
Yuxuan Chen	Yuxuan is an MS student as of Fall 2024, with interests at the intersections of Human-Computer Interaction and AI in education. His current research explores ways to enhance learning experiences in introductory CS courses for non-CS major engineering students. He is also the cofounder of AristAI, an AI Teaching Assistant designed to help course instructors by accurately and promptly answering student questions. Advisor: Mariana Silva
Mehmet Arif Demirtas	Arif joined in Fall 2023 as a PhD student. He is interested in exploring which skills learners from diverse backgrounds develop in programming education and how methods from human-computer interaction and machine learning could be utilized to address obstacles learners face while acquiring these skills to tackle inequalities in computing education. Advisor: Katie Cunningham
Salma El Otmani	Salma joined the PhD program in Fall 2024. Her research lies at the intersection of CS education and AI, examining how AI, especially large language models, can scaffold learning to broaden access and help students do more while deepening understanding. She designs empirical studies and evidence-informed tools that tailor support to students’ goals (for example, conversational programmers vs. end users) and prior knowledge, from novice to advanced. Advisor: Katie Cunningham
Kangyu Feng	Kangyu is a Master’s student starting fall of 2024. His research focuses on improving the learning experience in introductory computer science courses tailored for engineering students from non-CS backgrounds. Dedicated to enhancing online learning, Kangyu is also exploring innovative assessment tools for introductory courses in disciplines beyond computer science. Advisor: Mariana Silva
Lucas Flygare	Lucas joined as a PhD student in the fall of 2022. He is interested in exam frequency and second-chance testing, and how these factors can impact students’ study behavior and performance. Additionally, Lucas is exploring how large language models (LLMs) can be utilized to assist students in their classes based on their previous performance with the material. Advisor: Craig Zilles
Mohammed Hassan	Mohammed is interested in how educational tools and assessment tools affect students solving programming problems throughout the skill hierarchy. Current work includes understanding how students solve problems differently on paper vs computer assessments, and identifying difficulty factors in understanding code besides constructs and the hierarchy. Advisor: Craig Zilles
Shan Huang	Shan is broadly interested in how educational tools could help improve student learning. Current work includes improving students’ learning in cybersecurity with educational games and accessing students’ knowledge of cybersecurity concepts. Shan is also involved in various educational data mining projects. Advisor: Geoffrey Herman
Jina Hur	Jina is a PhD student interested in broadening participation and promoting inclusion in computing. Her current research involves identifying factors associated with motivation and persistence in CS among non-CS majors and individuals with disabilities, as well as designing interventions and technologies for these learner groups to provide engaging, effective, and inclusive CS learning environments. She is also interested in computing education policy. Advisor: Katie Cunningham
Jacob Levine	Jake Levine started the MS in 2024. His main research interests involve building reliable and explainable AI systems to better assist students and simplify course development. Advisor: Mariana Silva
Carlos Aldana Lira	Carlos joined the Ph.D. program in Fall 2025. In his research, he focuses on helping learners make sense of and explain how opaque computing systems work in their everyday lives through the lens of computer science.
Sofia Meyers	Sofia is currently pursuing a Ph.D in the Computer Science and Education Department. Her current research project aims to help students in non-data science fields learn the relevance of data science with the help of Large Language Models. Her previous research projects include work on STEM-C project and Eye-Tracking projects. The STEM-C project goal was to educate middle and high school students introductory computer science and data science skills in informal education settings. The Eye-Tracking project explored how programming experience affects code eye gaze patterns. Advisor: Chad Lane
Zepei Li	Zepei is a PhD student as of Fall 2024, and he is broadly interested in AI in education, academic integrity, and office hours optimization. His current research focuses on user studies exploring generative AI usage in engineering education. He aims to adapt AI tools to better serve educational purposes. Advisor: Abdu Alawini
Joslyn Orgill	Joslyn is a PhD student beginning in Fall 2025. After three years in industry, she decided to pursue her passion for broadening participation in computing and making programming more accessible. Her research explores how AI can support non-CS majors in becoming conversationally fluent in programming.
Christopher Perdriau	Christopher is interested in identifying, learning, and understanding the systemic barriers and stereotypes that are systematically preventing groups of people with diverse genders, races, cultures, and background from participating in computer science (CS). Advisor: Colleen Lewis
Laxmi Vijayan	Laxmi is a first-year PhD student whose current work explores how reflective writing can foster engagement, belonging, and growth in introductory programming. More broadly, their research sits at the intersection of HCI and CS education, where they design and evaluate tools to support programming, creativity, and metacognition.
Andrea Watkins	Andrea’s research centers on broadening participation in computing with a particular focus on media’s potential to improve diversity, equity, and access in computing. She investigates how computer science is represented in media– including television, film, podcasts, and social media– and how these representations may influence or discourage students from pursuing and participating in computer science. Advisor: Colleen Lewis
Batya Zamansky	Batya is a PhD Student interested in how students learn topics to prepare them for industry. Her current research is focused on student behaviors in creating and reviewing pull requests in the context of a course focused on working in a large codebase.
Victor Zhao	Victor joined as a Master student in Fall 2023 and a PhD student in Fall 2025. His interest lies in building educational tools to improve student learning, with autograding tools being the main focus. His current research involves building and evaluating an Automatic Short Answer Grading system to promote instant feedback in various types of assessments, and studying how and when the feedback can be helpful. Advisor: Mariana Silva

Recent Graduates

Liia Butler	Graduated May 2025! Liia is broadly interested in the learning and teaching of computer science, specifically focusing on software testing and debugging education and collaborative group work. Current projects explore how students test and debug their code and novel approaches to teaching debugging. Advisor: Geoffrey Herman First position: Assistant Teaching Professor, Department of Computer Science & Engineering, University of Minnesota
Chinny Emeka	Graduated December 2024! Chinny works on improving assessments for CS and other STEM students. He is also interested in leveraging AI in the educational context to grade work and provide feedback to students. Advisor: Craig Zilles
Morgan Fong	Graduated May 2025! Morgan is broadly interested in creating inclusive classroom environments. Currently, she studies how students’ sense of belonging develops in computing courses, particularly in collaborative learning environments. Past projects include creating an observation protocol for collaborative learning, developing coursework for in-service K-12 teachers, and exploring the relationship between spatial ability and data structures diagrams. Advisor: Geoffrey Herman First position: Assistant Professor of Instruction, Computer Science, University of Texas at Austin
Max Fowler	Graduated May 2024! Max is broadly interested in students’ knowledge of programming patterns and the hierarchy of skills novices need to learn to code. Max’s dissertation work included evaluation of novel machine learning auto-grading techniques for “Explain in Plain English” questions and investigating how changes in programming question surface features impact question difficulty and student performance. Advisor: Craig Zilles First position: Assistant Teaching Professor, Computer Science, University of Illinois Urbana-Champaign
Kathleen Isenegger	Graduated May 2025! Kathleen is interested in broadening participation in computing. Currently, she is engaged in work studying students’ sense of belonging in CS and social-psychological interventions to change students’ beliefs about the potential to do social good and collaborate with others in a computing career. Advisor: Colleen Lewis First position: Assistant Professor, Computer Science, California State University Chico
Suleman Mahmood	Suleman is broadly interested in developing assessments and tools that help students learn different computer science concepts. Suleman’s dissertation work included investigating why students struggle in learning the concepts related to computer memory organization and developing tools to create formative and summative assessments for cache memories. Advisor: Geoffrey Herman First position: Instructional Assistant Professor, Computer Science & Engineering, Texas A&M University
Vidushi Ojha	Graduated May 2024! Vidushi is interested in improving diversity, equity, and access in computing, as well as understanding how student success and well-being is affected by their environment. Her dissertation work examined the institutional policies and classroom practices that foster students’ learning and well-being, including transparent teaching practices and requiring an introductory computing course. Advisor: Colleen Lewis First position: Assistant Professor, Computer Science, Harvey Mudd College
Seth Poulsen	Graduated May 2023! Seth is interested in Computing Education, Programming Language design and implementation, Math Education, and any interesting intersections of the above. Seth’s dissertation research focused on improving the teaching and learning of mathematical proofs, specifically using the Proof Blocks tool which he created. Seth has also been involved in various educational data mining projects. Advisor: Geoffrey Herman First position: Assistant Professor, Computer Science, Utah State University
David Smith	Graduated May 2025! David has a broad interest in leveraging theories of learning to develop tools and activities that support novice programmers in learning how to code. He also applies theories of measurement to evaluate the effectiveness of these tools in measuring students programming skills. His research focuses on evaluating the design and use of Parsons problems for both formative and summative contexts. Most recently, he has been working on tools which aim to develop novices’ prompting and code comprehension skills to support them in successfully engaging in Human-GenAI collaborative coding. This includes using LLMs to auto-grade Explain in Plain Language (EiPL) activities and evaluating their use in multilingual contexts. Advisor: Craig Zilles First position: Assistant Professor, Computer Science, Virginia Tech
Sophia Yang	Graduated December 2024! Sophia is interested in Computing Education, Database Systems, Bioinformatics algorithms, Human Computer Interaction, and interesting intersections of the above. Current research work focuses on quantitatively and qualitatively studying how students learn SQL by utilizing Bioinformatics alignment algorithms. Her work aims to better improve students’ learning and instructors’ teaching experiences in large-scale database courses. Advisors: Abdu Alawini & Geoffrey Herman First position: Data Scientist, Microsoft