Mamba4KT:An Efficient and Effective Mamba-based Knowledge Tracing Model

TL;DR

Mamba4KT is a novel knowledge tracing model that significantly improves training and inference efficiency and resource utilization while maintaining prediction accuracy, addressing scalability challenges in smart education.

Contribution

This paper introduces Mamba4KT, the first model to focus on efficiency and resource utilization in knowledge tracing, with enhanced interpretability at sequence and exercise levels.

Findings

Achieves comparable accuracy to state-of-the-art models

Significantly improves training and inference efficiency

Enhances resource utilization and interpretability

Abstract

Knowledge tracing (KT) enhances student learning by leveraging past performance to predict future performance. Current research utilizes models based on attention mechanisms and recurrent neural network structures to capture long-term dependencies and correlations between exercises, aiming to improve model accuracy. Due to the growing amount of data in smart education scenarios, this poses a challenge in terms of time and space consumption for knowledge tracing models. However, existing research often overlooks the efficiency of model training and inference and the constraints of training resources. Recognizing the significance of prioritizing model efficiency and resource usage in knowledge tracing, we introduce Mamba4KT. This novel model is the first to explore enhanced efficiency and resource utilization in knowledge tracing. We also examine the interpretability of the Mamba structure both sequence-level and exercise-level to enhance model interpretability. Experimental findings across three public datasets demonstrate that Mamba4KT achieves comparable prediction accuracy to state-of-the-art models while significantly improving training and inference efficiency and resource utilization. As educational data continues to grow, our work suggests a promising research direction for knowledge tracing that improves model prediction accuracy, model efficiency, resource utilization, and interpretability simultaneously.