A Self-Attentive model for Knowledge Tracing (1907.06837v1)

Abstract: Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized learning platform to students. In recent years, methods based on Recurrent Neural Networks (RNN) such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) outperformed all the traditional methods because of their ability to capture complex representation of human learning. However, these methods face the issue of not generalizing well while dealing with sparse data which is the case with real-world data as students interact with few KCs. In order to address this issue, we develop an approach that identifies the KCs from the student's past activities that are \textit{relevant} to the given KC and predicts his/her mastery based on the relatively few KCs that it picked. Since predictions are made based on relatively few past activities, it handles the data sparsity problem better than the methods based on RNN. For identifying the relevance between the KCs, we propose a self-attention based approach, Self Attentive Knowledge Tracing (SAKT). Extensive experimentation on a variety of real-world dataset shows that our model outperforms the state-of-the-art models for knowledge tracing, improving AUC by 4.43% on average.

Analysis of "A Self-Attentive Model for Knowledge Tracing"

The paper entitled "A Self-Attentive Model for Knowledge Tracing" authored by Shalini Pandey and George Karypis, proposes a novel approach to knowledge tracing using a self-attentive model (SAKT). This model addresses the limitations faced by existing recurrent neural network (RNN) based techniques, especially when dealing with sparse datasets prevalent in real-world educational settings.

Background and Motivation

Knowledge Tracing (KT) involves modeling a student's mastery over knowledge concepts (KCs) by analyzing their past learning activities. The principal objective is to predict future performance, thereby enabling personalized learning paths. Traditional methods have seen substantial improvements with the advent of deep learning models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN). However, these RNN-based models often struggle with data sparsity, a common characteristic of educational datasets where students engage with a limited subset of KCs.

Methodology and Model Design

The authors introduce SAKT, which employs the Transformer architecture's self-attention mechanism to address these challenges. Unlike RNNs that process input sequentially, this model exploits the parallelizability and memory-efficiency of self-attention to better handle sparse data. Crucially, SAKT identifies and weighs relevant past activities, focusing on those that most accurately predict future performance.

The architecture comprises of several layers that transform and process input sequences, namely:

• Embedding Layer: Transforms input sequences into fixed-length vectors suitable for further processing.
• Self-Attention Layer: Employs scaled dot-product attention to identify relevant historical activities, based on the relevance of learning activities to the current task.
• Feed-Forward Layer: Introduces non-linearity into the model, enhancing its ability to capture complex interactions.

The choice of self-attention over RNNs enables the model to effectively manage varying sequence lengths and interactions, leading to improved predictions.

Experimental Evaluation

The authors validate their approach using multiple datasets including real-world educational datasets (ASSISTment 2009, ASSISTment 2015, etc.) and a synthetic dataset. The primary evaluation metric is the Area Under the Receiver Operating Characteristic Curve (AUC), a standard measure in binary classification tasks like KT.

SAKT demonstrates superior performance, achieving a notable improvement in AUC by an average of 4.43% compared to state-of-the-art methods. The model's ability to parallelize computations also results in significantly faster training times, by an order of magnitude compared to RNN-based methods.

Implications and Future Prospects

The model's enhanced performance on sparse datasets underscores the efficacy of the self-attention mechanism in educational applications. By improving prediction accuracy and training efficiency, SAKT could facilitate the development of more responsive and personalized learning platforms.

The findings open avenues for further exploration of self-attentive models in knowledge tracing, particularly in understanding and modeling learning trajectories over time. Future work may focus on integrating advanced representations of student learning behaviors or incorporating features that model specific educational interventions such as hint-taking behavior.

In summary, the paper presents a significant advancement in the field of educational data mining, leveraging recent innovations in deep learning architectures to address long-standing challenges in knowledge tracing. The deployment of self-attention mechanisms holds promise for enhancing predictive capabilities and supporting the creation of more effective educational tools.