HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge Tracing

TL;DR

This paper introduces HiTSKT, a hierarchical transformer model that effectively captures student learning dynamics within and across sessions, significantly improving knowledge tracing accuracy in online education.

Contribution

The paper proposes a novel hierarchical transformer architecture with session-level and interaction-level encoders, incorporating a power-law-decay attention mechanism for better modeling of long-term forgetting.

Findings

HiTSKT outperforms six state-of-the-art models on three public datasets.

The model effectively captures sessional shifts and long-term forgetting.

Hierarchical structure improves knowledge state prediction accuracy.

Abstract

Knowledge tracing (KT) aims to leverage students' learning histories to estimate their mastery levels on a set of pre-defined skills, based on which the corresponding future performance can be accurately predicted. As an important way of providing personalized experience for online education, KT has gained increased attention in recent years. In practice, a student's learning history comprises answers to sets of massed questions, each known as a session, rather than merely being a sequence of independent answers. Theoretically, within and across these sessions, students' learning dynamics can be very different. Therefore, how to effectively model the dynamics of students' knowledge states within and across the sessions is crucial for handling the KT problem. Most existing KT models treat student's learning records as a single continuing sequence, without capturing the sessional shift of students' knowledge state. To address the above issue, we propose a novel hierarchical transformer model, named HiTSKT, comprises an interaction(-level) encoder to capture the knowledge a student acquires within a session, and a session(-level) encoder to summarise acquired knowledge across the past sessions. To predict an interaction in the current session, a knowledge retriever integrates the summarised past-session knowledge with the previous interactions' information into proper knowledge representations. These representations are then used to compute the student's current knowledge state. Additionally, to model the student's long-term forgetting behaviour across the sessions, a power-law-decay attention mechanism is designed and deployed in the session encoder, allowing it to emphasize more on the recent sessions. Extensive experiments on three public datasets demonstrate that HiTSKT achieves new state-of-the-art performance on all the datasets compared with six state-of-the-art KT models.