Language Model Can Do Knowledge Tracing: Simple but Effective Method to Integrate Language Model and Knowledge Tracing Task
This work addresses the challenge of modeling student knowledge over time for online learning platforms by incorporating semantic information, representing an incremental improvement over existing deep learning-based methods.
The paper tackles the problem of knowledge tracing in online learning by integrating pre-trained language models to leverage textual information from questions and concepts, resulting in significant performance improvements on benchmark datasets and effectively addressing the cold-start problem.
Knowledge Tracing (KT) is a critical task in online learning for modeling student knowledge over time. Despite the success of deep learning-based KT models, which rely on sequences of numbers as data, most existing approaches fail to leverage the rich semantic information in the text of questions and concepts. This paper proposes Language model-based Knowledge Tracing (LKT), a novel framework that integrates pre-trained language models (PLMs) with KT methods. By leveraging the power of language models to capture semantic representations, LKT effectively incorporates textual information and significantly outperforms previous KT models on large benchmark datasets. Moreover, we demonstrate that LKT can effectively address the cold-start problem in KT by leveraging the semantic knowledge captured by PLMs. Interpretability of LKT is enhanced compared to traditional KT models due to its use of text-rich data. We conducted the local interpretable model-agnostic explanation technique and analysis of attention scores to interpret the model performance further. Our work highlights the potential of integrating PLMs with KT and paves the way for future research in KT domain.