MemoryKT: An Integrative Memory-and-Forgetting Method for Knowledge Tracing

TL;DR

MemoryKT introduces a comprehensive memory and forgetting model for knowledge tracing, using a temporal variational autoencoder to simulate individual memory dynamics and improve prediction accuracy.

Contribution

It proposes a novel three-stage memory modeling approach with personalized forgetting, enhancing interpretability and performance in knowledge tracing.

Findings

Outperforms state-of-the-art models on four datasets

Effectively models individual differences in memory dynamics

Improves interpretability of student knowledge states

Abstract

Knowledge Tracing (KT) is committed to capturing students' knowledge mastery from their historical interactions. Simulating students' memory states is a promising approach to enhance both the performance and interpretability of knowledge tracing models. Memory consists of three fundamental processes: encoding, storage, and retrieval. Although forgetting primarily manifests during the storage stage, most existing studies rely on a single, undifferentiated forgetting mechanism, overlooking other memory processes as well as personalized forgetting patterns. To address this, this paper proposes memoryKT, a knowledge tracing model based on a novel temporal variational autoencoder. The model simulates memory dynamics through a three-stage process: (i) Learning the distribution of students' knowledge memory features, (ii) Reconstructing their exercise feedback, while (iii) Embedding a personalized forgetting module within the temporal workflow to dynamically modulate memory storage strength. This jointly models the complete encoding-storage-retrieval cycle, significantly enhancing the model's perception capability for individual differences. Extensive experiments on four public datasets demonstrate that our proposed approach significantly outperforms state-of-the-art baselines.