Qualitative study of 19 CS students using multi-view visualizations reveals selective engagement driven by agency, fit, and legitimacy rather than cognitive load reduction alone.
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cs.HC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Randomized trial finds diverse LLM explanations improve open-ended accuracy by 7.7% over generic ones in introductory programming without raising cognitive load.
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Code as Anchor, Memory and Metaphor as Support: Learner Experiences with Multi-View Visualizations
Qualitative study of 19 CS students using multi-view visualizations reveals selective engagement driven by agency, fit, and legitimacy rather than cognitive load reduction alone.
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Exploring the Value of Diverse LLM Explanations in Introductory Programming
Randomized trial finds diverse LLM explanations improve open-ended accuracy by 7.7% over generic ones in introductory programming without raising cognitive load.