Interpretable Sequence Modeling of Educational Behavior using Temporal Attention Networks
DOI:
https://doi.org/10.26713/cma.v17i1.3339Keywords:
Dynamic Temporal Attention Network (DTAN), Educational data mining, Learning analytics, Temporal Convolutional Networks (TCN), Attention mechanisms, Sequence modeling, Student performance predictionAbstract
The increasing popularity of online learning platforms has led to vast amounts of sequential data regarding learner behavior. Available predictive models tend to focus on fixed features. They cannot pinpoint the dynamic temporal changes in learning activity, thereby reducing the effectiveness of predictive methods and making them more challenging to understand. In this work, the Dynamic Temporal Attention Network (DTAN) is proposed. This novel deep learning architecture learns e-learning behavior using time-aware attention and temporal convolution to enhance predictive accuracy and interpretability. TCN has already been combined with two significant attention modules. The Attention-over-Time windows (ATW) and the Dynamic Contextual Attention Mechanism (DCAM). With such components, the model can learn short and long-term dependencies on the learner’s behavior and adaptively prioritize critical time slots. To train and evaluate the model, two large-scale datasets are used: EdNet, containing over 130 million question and answer interactions in the K-12 context, and OULAD, an exam-taking dataset in the university context. By outperforming state-of-the-art models, including long short-term memory (LSTM) and gated recurrent units (GRU), as well as standard TCNs, TAN achieves significant improvements across diverse classification tasks. It has strong early prediction skills and an interpretable visualization that focuses on weight, highlighting critical incidents in a growing learner journey. Such observations are essential in the process of providing individual instruction and necessary intervention. DTAN delivers an interpretable solution for sequential data modeling in education, significantly boosting efficiency, particularly in adaptive learning systems.
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