Paper: https://arxiv.org/pdf/1907.06837.pdf

Code: https://github.com/shalini1194/SAKT/tree/master/2019-EDM

San Diego State University

1. Introduction
2. Proposed method
3. Experimental setting
4. Results and Analysis
5. Conclusion and Future Work

A Self-Attentive model for Knowledge Tracing

Experimental Setting (A Self-Attentive model for Knowledge Tracing)

1. Student Performance Prediction
2. Attention Weights Visualization
3. Ablation Study
4. Training Efficiency

Results and Analysis (A Self-Attentive model for Knowledge Tracing)

The proposed Self-Attentive model for Knowledge Tracing (SAKT) transforms student interaction sequences into a sequential modeling problem to predict whether a student will answer a question correctly. Its architecture consists of several key components: an embedding layer for interactions and exercises, a self-attention layer utilizing a scaled dot-product mechanism to assign weights to previous exercises, a feed-forward layer, residual connections, layer normalization, and a prediction layer.

Proposed Method (A Self-Attentive Model for Knowledge Tracing)

The Self-Attentive model for Knowledge Tracing (SAKT) is a transformer-based approach designed to predict student performance. Because a student's learning process heavily depends on their past experiences and acquired skills, SAKT uses a self-attention mechanism to assign weights to previously solved items, treating individual exercises as Knowledge Components (KCs). By weighting these past interactions, the model predicts the probability of a student answering a particular question correctly. In its initial proposal, SAKT demonstrated improved predictive performance over previous knowledge tracing models.

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