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20212026
most citedRobosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing

18 citations · 67 across the 39 of their papers we have counts for

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cs.HC2026

Pulla: A Parsons Problem Tool for Fine-Grained Behavioral Tracing and Instructor-Facing Problem-Solving Analysis

Daniel Prol, Juho Leinonen, Arto Hellas +2

Existing Parsons problem tools primarily focus on correctness, indicating whether a student solved a problem, but providing limited visibility into the underlying problem-solving p…

cs.HC2026

Exploring the Value of Diverse LLM Explanations in Introductory Programming

Seth Bernstein, Paul Denny, Juho Leinonen +4

Large Language Models (LLMs) have shown the potential to generate code explanations that surpass those of peers in quality, offering promising opportunities for computer science ed…

cs.HC2026

The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance

Tsvetomila Mihaylova, Evanfiya Logacheva, Arto Hellas +6

When programming students encounter errors in their code, compiler messages or static analysis output often provide limited guidance, particularly for novice programmers. Personali…

cs.HC2026

Personalized Worked Example Generation from Student Code Submissions Using Pattern-based Knowledge Components

Griffin Pitts, Muntasir Hoq, Peter Brusilovsky +4

Adaptive programming practice often relies on fixed libraries of worked examples and practice problems, which require substantial authoring effort and may not correspond well to th…

cs.HC2026

Fast and Forgettable: A Controlled Study of Novices' Performance, Learning, Workload, and Emotion in AI-Assisted and Human Pair Programming Paradigms

Nicholas Gardella, James Prather, Juho Leinonen +3

Code-generating Artificial Intelligence has gained popularity within both professional and educational programming settings over the past several years. While research and pedagogy…

cs.HC20251 cited

SPIRAL integration of generative AI in an undergraduate creative media course: effects on self-efficacy and career outcome expectations

Troy Schotter, Saba Kawas, James Prather +3

Computing education and computing students are rapidly integrating generative AI, but we know relatively little about how different pedagogical strategies for intentionally integra…