Papers with per-student-problem pair level

    1 papers
    KASER: Knowledge-Aligned Student Error Simulator for Open-Ended Coding Tasks (2026.acl-long)

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    Challenge: coding tasks that provide detailed insights into student knowledge are challenging to train . open-ended tasks often suffer from mode collapse and fail to capture student errors .
    Approach: They propose a method that aligns errors with student knowledge by using a hybrid reward system.
    Outcome: The proposed method outperforms baselines on code and error prediction and error coverage and simulated code diversity on two real-world datasets.

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