Computer Science Education at Khoury College of Computer Sciences
Leading the way in teaching and broadening participation
Computing education studies how students learn computing, effective pedagogies for teaching computing, and helping diverse populations of students succeed in computing. At Khoury College of Computer Sciences, our research runs the gamut from broadening participation in computing through systemic changes; how to best train teaching assistants; teaching ethics in technology; studying how to incorporate generative AI into the introductory sequence; how computing can be combined with other fields to create interdisciplinary computing majors; how to create new pathways to the MS in computing; and national trends in curriculum and best practices in curricular design.
Impact statement – move to right of page, photo(s) on left
As part of our mission of CS for everyone, Khoury College is committed to making the study of computer science available and accessible to anyone interested in the field. This belief extends beyond the bounds of Northeastern to work we do on an ongoing basis with colleges and universities around the globe — sharing best practices and collaborating on research.
Sample research areas
- Interdisciplinary computing majors
- Systemic, sustainable changes to remove institutional barriers to expand student opportunity to discover persist in and graduate from computing programs
- The impact of generative AI in helping students learn to code
- Pathways to the MS in CS, DS, AI, and Cybersecurity for students who did not study computing as undergraduates
- Embedding ethics throughout the computing curriculum
- The structural complexity of computing programs nationally and the impact on broadening participation in computing
Current project highlights
Understanding and improving how beginning programmers interact with LLMs
Researchers examine fundamental questions of how beginning programmers interact with large language models (LLMs) in programming tasks. Do beginners struggle because they lack the right technical vocabulary, or because they omit crucial details LLMs need? Does the unpredictable nature of these models lead them to believe their prompts are more effective than they really are? And to what extent do beginners misunderstand the code that LLMs produce? Findings so far show that without appropriate training, beginners often make superficial and unhelpful changes to their prompts, don’t notice the importance of conveying full context, and struggle to interpret LLM-generated code. This work paves the way to develop new ways to teach students how to work with LLMs for programming tasks, and how to improve LLMs to better suit the needs of new programmers.
Investigating the design and implementation of CS curricula and their impact on student progress
Researchers investigate the design and implementation of computer science curricula and their impact on student participation and progress, particularly for students new to computing. We examine whether the placement of certain courses impacts students’ decisions to major in computer science or unintentionally creates barriers. We identify the courses necessary for students at various developmental stages and explore best practices in curriculum design. Additionally, we analyze retention rates across different degree programs and identify factors influencing course selection and student performance.
Developing and analyzing TA recruitment, training, and evaluation in higher ed
Khoury researchers’ work on teaching assistants (TAs) recognizes the important role they play for both students and faculty in computing programs. We focus on improving student support and TA preparedness with our work on developing, launching, and analyzing TA training programs, and on identifying areas for improvement with broader analysis of student, TA, and faculty experiences within this domain. In support of this work, we ask questions like what barriers are keeping students away from office hours? Are certain students more affected than others? How do we evaluate TA training, and do TAs benefit from being evaluated? Can we identify areas of bias in our TA pipeline and how might we address them? What other factors, such as extracurricular experiences, may be affecting who chooses to TA and how TAs are chosen?
Understanding and transforming CS education in higher ed
Researchers study the landscape of how computer science is offered, what math requirements are common, how universities are adapting to offer more AI with an understanding of the myriad university contexts. We study the impact of systemic, sustainable changes to introductory sequence in CS, to student support and to the major. A recent focus is a 10-university study of how interdisciplinary computing degrees can be effectively implemented and their impact on students and the university writ large.
Recent research publications
Does Reducing Curricular Complexity Impact Student Success in Computer Science?
Authors: Sumukhi Ganesan, Albert Lionelle, Catherine Gill, Carla Brodley (Proceedings of SIGCSE 2025)
Computer science degree requirements often have a rigid pre- and corequisite structure, which can impede a student’s progression through a degree, and in particular can add one or more semesters to degree time to completion. We present the results of a comparative analysis of curricula before and after a major structural revision. The first curriculum adheres to the conventional rigid prerequisite structure, while the second emphasizes student choice and multiple pathways through the degree. No changes were made to the course content/outcomes between the two versions. The new curriculum, with a 60% reduction in curricular structural complexity, showed both increased retention of students over the old curriculum (67% to 98%) and an increase in the number of students converting from undeclared to computer science (44% to 69%).
An MS in CS for Non-CS Majors: A Ten-Year Retrospective
Authors: Logan W. Schmidt, Caitlin J. Kidder, Ildar Akhmetov, Megan Bebis, Alan C. Jamieson, Albert Lionelle, Sarah Maravetz, Sami Rollins, Ethan Selinger (Proceedings of SIGCSE 2025)
For the last 10 years, Northeastern University has offered a two-semester bridge into a master’s in computer science for people with undergraduate degrees in non-computing disciplines. The bridge program has over 2,000 currently enrolled students with more than 50% women every year since 2020, and domestic enrollment has increased relative to direct-entry master’s students. Our data show that bridge students, including those with non-STEM backgrounds, perform comparably to direct-entry students in terms of GPA and job outcomes.
An Analysis of the Math Requirements of 199 CS BS/BA Degrees at 158 US Universities
Authors: Carla E. Brodley, McKenna Quam, and Mark Weiss (Communications of the ACM, 2024)
For at least 40 years, there has been debate and disagreement as to the role of mathematics in the computer science curriculum. This paper presents the results of an analysis of the math requirements of 199 computer science (CS) BS/BA degrees from 158 universities, looking not only at which math classes are required, but at how they are used as prerequisites (and corequisites) for CS courses.
Teaching Assistant Training: An Adjustable Curriculum for Computing Disciplines
Authors: Felix Muzny, Michael D. Shah (Proceedings of SIGCSE 2023)
We present an adaptable curriculum for training undergraduate and graduate teaching assistants (TAs) in computing disciplines that is modular, synchronous, and explicitly mirrors the teaching techniques that are used in our classes. Our curriculum is modular, with each component able to be expanded or compressed based on institutional needs and resources. It is appropriate for TAs from CS1 through advanced computing classes.
Substance Beats Style: Why Beginning Students Fail to Code with LLMs
Authors: Francesca Lucchetti, Zixuan Wu, Arjun Guha, Molly Q Feldman, and Carolyn Jane Anderson (Annual Conference of the Nations of the Americas Chapter of the ACL (NAACL), 2025)
Although LLMs are increasing the productivity of professional programmers, existing work shows that beginners struggle to prompt LLMs to solve text-to-code tasks. Why is this the case? This paper explores two competing hypotheses about the cause of student-LLM miscommunication: (1) students simply lack the technical vocabulary needed to write good prompts, and (2) students do not understand the extent of information that LLMs need to solve code generation tasks. We find that substance beats style: a poor grasp of technical vocabulary is merely correlated with prompt failure; that the information content of prompts predicts success; that students get stuck making trivial edits; and more.
Related labs and groups
Faculty members
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Carla E. Brodley
Carla Brodley is the dean of inclusive computing at Khoury College and the founding executive director of Northeastern’s Center for Inclusive Computing, which aims to remove barriers to participation in the field. She was dean of Khoury College from 2014 to 2021.
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Adeel Bhutta
Adeel A. Bhutta is an associate teaching professor at Khoury College. His research primarily focuses on image processing and computer vision, and he is currently working on selective subtraction and deep learning. A recipient of numerous awards, he has taught a wide range of courses during his teaching career, including ones he developed.
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Arjun Guha
Arjun Guha is an associate professor at Khoury College. His programming languages research addresses security and reliability problems in web programming, systems, and robotics.
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Albert Lionelle
Albert Lionelle is an associate teaching professor at Khoury College, and the director of the Align Online program. His research centers on the use of computing tools and inclusive pedagogy to enable better computer science education.
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Felix Muzny
Felix Muzny is a clinical instructor and the director of teaching assistants at Khoury College. They care deeply about making computing classrooms more welcoming for all students, and their teaching and research both focus on computing education, digital humanities, and the intersection of sociology, ethics, and computing.
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Logan Schmidt
Logan Schmidt is an assistant teaching professor at Khoury College, and the assistant director of computing programs for the Vancouver campus. He is a graduate of the Align program himself, and in both his research and teaching work he aims to introduce newcomers to computer science fundamentals in a way that helps them effectively build new careers.