AI 中文总结
本研究提出CE-KT模型，利用回答过程特征推导行为代理分数，调节LSTM状态更新，在ASSISTments数据集上表现优于多种行为融合模型，可优化知识追踪中对回答正确性的解释。
AI 中文摘要
知识追踪模型通常将回答正确性作为估计学生潜在知识状态的核心观测指标。然而，相同的正确或错误回答可能源于不同的行为情境，例如快速猜测、使用提示或重复尝试。将正确性视为具有统一信息性的指标，可能会给循环状态更新引入歧义。本研究提出了基于正确性条件的证据感知知识追踪模型（Correctness-conditioned Evidence-aware Knowledge Tracing, CE-KT），该模型利用可观测的回答过程特征来调节正确性写入循环状态的方式。CE-KT从回答时间、提示使用情况、尝试次数和行为历史中推导弱监督的行为代理分数，这些分数作为行为信号，而非掌握程度、回答质量或认知状态的直接衡量指标。随后，CE-KT利用当前的正确性选择正确回答门或错误回答门，所选门会同时调节LSTM的隐藏状态和细胞状态，调节后的状态会反馈到后续的循环更新中。在ASSISTments数据集上的实验表明，行为条件分数与固定正确性组内未来相同技能的表现相关，尤其在错误交互中更为明显。CE-KT在主要预测指标上总体优于多种行为融合替代模型，尽管其校准优势并不稳定。消融分析为正确性特定的循环调节和循环反馈提供了部分支持。这些发现表明，行为信息可用于调节对知识追踪中回答正确性的解释，但所提出的代理分数不应被视为真正掌握程度或因果学习效应的直接证据。
英文摘要
Knowledge tracing models usually use response correctness as a central observation for estimating students' latent knowledge states. However, the same correct or incorrect response may arise from different behavioral contexts, such as rapid guessing, hint use, or repeated attempts. Treating correctness as uniformly informative may therefore introduce ambiguity into recurrent state updates. This study proposes Correctness-conditioned Evidence-aware Knowledge Tracing (CE-KT), which uses observable response-process features to condition how correctness is written into recurrent states. CE-KT derives weakly supervised behavioral proxy scores from response time, hint use, attempt count, and behavioral history. These scores are used as behavioral signals, not as direct measures of mastery, response quality, or cognitive state. CE-KT then uses current correctness to select a correct-response or incorrect-response gate. The selected gate modulates both the LSTM hidden state and cell state, and the modulated states are fed back into later recurrent updates. Experiments on ASSISTments data show that behavioral condition scores are associated with future same-skill performance within fixed correctness groups, especially for incorrect interactions. CE-KT generally outperforms several behavior-fusion alternatives on the main predictive metrics, although its calibration advantage is not consistent. Ablation analyses provide partial support for correctness-specific recurrent modulation and recurrent feedback. These findings suggest that behavioral information can help condition the interpretation of response correctness in knowledge tracing, but the proposed proxy scores should not be treated as direct evidence of true mastery or causal learning effects.
Comments24 pages, 2 figures, 11 tables. Preprint
