Correctness Is Not Homogeneous Evidence: A Correctness-conditioned Evidence-aware Knowledge Tracing Model

Abstract

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.