Towards Robust Knowledge Tracing Models via k-Sparse Attention (2407.17097v1)

Abstract: Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interaction sequences. With the advanced capability of capturing contextual long-term dependency, attention mechanism becomes one of the essential components in many deep learning based KT (DLKT) models. In spite of the impressive performance achieved by these attentional DLKT models, many of them are often vulnerable to run the risk of overfitting, especially on small-scale educational datasets. Therefore, in this paper, we propose \textsc{sparseKT}, a simple yet effective framework to improve the robustness and generalization of the attention based DLKT approaches. Specifically, we incorporate a k-selection module to only pick items with the highest attention scores. We propose two sparsification heuristics : (1) soft-thresholding sparse attention and (2) top-$K$ sparse attention. We show that our \textsc{sparseKT} is able to help attentional KT models get rid of irrelevant student interactions and have comparable predictive performance when compared to 11 state-of-the-art KT models on three publicly available real-world educational datasets. To encourage reproducible research, we make our data and code publicly available at \url{https://github.com/pykt-team/pykt-toolkit}\footnote{We merged our model to the \textsc{pyKT} benchmark at \url{https://pykt.org/}.}.

Towards Robust Knowledge Tracing Models via k-Sparse Attention

The paper "Towards Robust Knowledge Tracing Models via k-Sparse Attention" introduces a novel approach aimed at improving the robustness and generalization of attention-based Deep Learning Knowledge Tracing (DLKT) models. These models are essential for predicting students' future performance based on their historical interaction data. The proposed framework, referred to as sparseKT, enhances traditional attention mechanisms by incorporating sparse attention techniques to focus on a limited set of influential interactions. This is particularly beneficial in educational data scenarios that are characterized by sparsity and potential overfitting.

Key Contributions and Methods

Embedding Enhancement

The sparseKT model begins by improving the interaction representation aspect of DLKT models. Inspired by the Rasch model in psychometrics, which uses a scalar to highlight question discrimination, the authors propose an enriched representation that incorporates a question-specific discrimination factor. This factor aims to capture the unique difficulty and characteristics of each question, thus providing a more nuanced understanding of the interactions.

k-Sparse Attention Mechanisms

Two primary sparsification strategies are proposed: soft-thresholding sparse attention and top-K sparse attention. Both methods start by calculating attention scores using a modified version of the self-attentive mechanism. The soft-thresholding approach gradually selects top scores until their cumulative sum exceeds a predetermined threshold, whereas the top-K approach selects exactly the top K interactions based on attention scores. This targeted focus helps eliminate noise from irrelevant interactions, hence improving the overall model's prediction accuracy.

Experimental Validation

The model was evaluated on three widely recognized educational datasets: ASSISTments2015 (AS2015), NeurIPS2020 Education Challenge (NIPS34), and Peking Online Judge (POJ). The datasets cover a wide range of educational contexts, providing a robust testing ground for sparseKT. The proposed model was compared against 11 established KT models, including DKT, DKT+, SAKT, and AKT.

Empirical Results

• Performance Metrics: The sparseKT model showed substantial improvements in AUC and accuracy metrics, particularly on the NIPS34 and POJ datasets where it consistently ranked within the top 3. For instance, the sparseKT-soft variant improved AUC by up to 4.79% on average when compared to standard attention-based models like SAKT.
• Sparsity Level Impact: An ablation paper revealed that increasing the sparsity level (i.e., the value of k) generally improved performance up to a point, after which the model’s capabilities plateaued or slightly decreased. This indicates an optimal range for the sparsity level, balancing between capturing enough relevant interactions and minimizing noise.
• KC Relations: Visualization of knowledge component (KC) interactions derived from sparse attention scores demonstrated that the model effectively captured valuable dependencies between KCs, offering insights into how past interactions influence future performance predictions.

Implications and Future Directions

The sparseKT model’s ability to improve upon traditional attention mechanisms has significant implications for educational technology and adaptive learning systems. By focusing on the most influential interactions, sparseKT not only enhances predictive performance but also potentially offers better interpretability regarding student learning behaviors and knowledge state transitions.

From a theoretical standpoint, the incorporation of sparse attention mechanisms could be extended to other domains within artificial intelligence where data sparsity and overfitting are commonly encountered issues. Future research directions may include the exploration of dynamic or adaptive sparse attention methods that can self-tune the sparsity parameter $k$ based on the context of the input data.

In conclusion, the paper presents a significant advance in the field of knowledge tracing, leveraging sparse attention mechanisms to enhance robustness and predictive capability. The sparseKT framework offers a promising avenue for further research and application in educational and other AI-related areas.