A difficulty-aware conversational knowledge tracing framework that combines LLMs with Item Response Theory to produce interpretable student performance predictions in tutor dialogues.
arXiv preprint arXiv:2310.10648 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Randomized classroom trial of 215 students shows natural language LLM feedback improves completion rates and convergence speed over test-case feedback or none, with test-case effects varying by validity.
citing papers explorer
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Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
A difficulty-aware conversational knowledge tracing framework that combines LLMs with Item Response Theory to produce interpretable student performance predictions in tutor dialogues.
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A Classroom Study of LLM-Generated Feedback Intervention in Introductory Programming
Randomized classroom trial of 215 students shows natural language LLM feedback improves completion rates and convergence speed over test-case feedback or none, with test-case effects varying by validity.