Narges Norouzi
University of California-Berkeley, CA·Education Research
How can computing education adapt to each student, both inside the classroom and in the support systems around it? Students arrive with very different backgrounds and levels of preparation, and many begin at community colleges before transferring to a university.
- Fields
Education Research
Computer Science
- Topics
computer science education
computing education
large language models
- Grants
- 2 known grants, through May 2028
What they work on
- Model students' problem-solving strategies and algorithmic thinking in computer science courses by integrating fine-grained process data to trace competencies across levels of abstraction.
- Identify effective and harmful learning patterns and determine how students' interactions with intelligent learning systems affect their competence.
- Generate feedback, worked examples and suggested next problems with large language models combining fine-tuning and reinforcement learning with human feedback (RLHF), evaluated for relevance, fit to student competency and pedagogical helpfulness.
- Identify institutional barriers to computing transfer student success by applying and refining a servingness framework across the University of California and partnering community colleges.
- Implement and evaluate advising, tutoring, mentoring, research and professional development practices for transfer students, measuring effects on persistence, degree completion and career placement.
More about the research
Tools that adjust to what a learner knows, and institutions that adjust to the paths students actually take, could help more of them finish computing degrees. Two projects address this at these two levels: one uses artificial intelligence (AI) to personalize programming instruction, the other studies how universities can better serve computing transfer students.
Why it matters
Programming students could receive AI-generated feedback and practice problems matched to what they actually know, which may help close gaps among students with different levels of preparation. Colleges could use evidence-based practices to help community college transfer students graduate in computing, enter graduate study and find jobs, including in areas such as artificial intelligence and data science.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
CUE-P: Establishing Servingness in Computing through Baskin Engineering Excellence Scholars Program
NSF · Dec 2025 – May 2028
Collaborative Research: NSF-AoF: HCC: Small: Advanced Student Modeling and Tailored Large Language Models for Personalized Learning in Computer Science Education
NSF · Jan 2025 – Dec 2027
Closest labs
Carla Brodley
Northeastern University, MA·Education Research
Test whether a placement assessment plus one-credit supplemental courses can reduce credit loss for community college transfer students entering computer science majors, in a pilot at three universities.
Andrew Watkins
Case Western Reserve University, OH·Education Research
Develop a shared curriculum for the first two years of computer science across 15 Ohio colleges, and use it to create formal articulation agreements so community college credits transfer cleanly.
Maciej Pankiewicz
University of Pennsylvania, PA·Education Research
Extend the Decimal Point mathematics learning game so that a large language model generates hints personalized to each student's problem-solving history, misconceptions and in-game learning behaviors.
The school
University of California-Berkeley: 46 labs on PI Pages, $1.1B a year on research, 825 research doctorates a year.
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