CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer

TL;DR

This paper introduces CLST, a novel approach that uses a generative language model to improve knowledge tracing, especially in cold-start scenarios with limited student data, by framing KT as a natural language processing task.

Contribution

The study proposes a framework that aligns a generative LLM as a student knowledge tracer, effectively addressing cold-start issues in knowledge tracing.

Findings

CLST outperforms baseline models in data-scarce settings

Significant improvements in prediction accuracy, reliability, and generalization

Effective across multiple subjects like math, social studies, and science

Abstract

Knowledge tracing (KT), wherein students' problem-solving histories are used to estimate their current levels of knowledge, has attracted significant interest from researchers. However, most existing KT models were developed with an ID-based paradigm, which exhibits limitations in cold-start performance. These limitations can be mitigated by leveraging the vast quantities of external knowledge possessed by generative large language models (LLMs). In this study, we propose cold-start mitigation in knowledge tracing by aligning a generative language model as a students' knowledge tracer (CLST) as a framework that utilizes a generative LLM as a knowledge tracer. Upon collecting data from math, social studies, and science subjects, we framed the KT task as a natural language processing task, wherein problem-solving data are expressed in natural language, and fine-tuned the generative LLM using the formatted KT dataset. Subsequently, we evaluated the performance of the CLST in situations of data scarcity using various baseline models for comparison. The results indicate that the CLST significantly enhanced performance with a dataset of fewer than 100 students in terms of prediction, reliability, and cross-domain generalization.