Addressing two problems in deep knowledge tracing via prediction-consistent regularization

By Chun-Kit Yeung, Dit-Yan Yeung. ACM Conference on Learning @ Scale, 2018

341 citations recorded by the source corpus.

Knowledge tracing is one of the key research areas for empowering personalized education. It is a task to model students' mastery level of a knowledge component (KC) based on their historical learning trajectories. In recent years, a recurrent neural network model called deep knowledge tracing (DKT) has been proposed to handle the knowledge tracing task and literature has shown that DKT generally outperforms traditional methods. However, through our extensive experimentation, we have noticed two major problems in the DKT model. The first problem is that the model fails to reconstruct the observed input. As a result, even when a student performs well on a KC, the prediction of that KC's mastery level decreases instead, and vice versa. Second, the predicted performance for KCs across time-steps is not consistent. This is undesirable and unreasonable because student's performance is expected to transit gradually over time. To address these problems, we introduce regularization terms that correspond to reconstruction and waviness to the loss function of the original DKT model to enhance the consistency in prediction. Experiments show that the regularized loss function effectively alleviates the two problems without degrading the original task of DKT.1

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/750c62a9b4d33623687506a66dbf5e97e4b9d7be

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Citations in the corpus

Mining Big Data in Education: Affordances and Challenges (2020, result): “…the other approaches, DKT is generally more effective at predicting student correctness during learning (Khajah et al., 2016; Yeung & Yeung, 2018), but it has not been used extensively in the real world due to limitations around interpretability and stability of estimates (Yeung & Yeung, 2018).”
Context-Aware Attentive Knowledge Tracing (2020, methodology): “…several baseline KT methods, including BKT+ [35], DKT, DKT+ (which is an improved version of DKT with regularization on prediction consistency [34]), DKVMN [36], and the recently proposed self-attentive KT (SAKT) method [18], which uses an attention mechanism that can be viewed as a special…”