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.
From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education
Enuma Inc（Enuma公司）；University of Memphis（孟菲斯大学）；Daegu National University of Education（大邱教育大学）；Seoul National University（首尔大学）；Seoul Metropolitan Office of Education（首尔市教育厅）；Korea University（高丽大学）
MERIT: Memory-Enhanced Retrieval for Interpretable Knowledge Tracing
School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；Shanghai Innovation Institute（上海创新研究院）；WeChat AI, Tencent（微信AI，腾讯）
East China Normal University（华东师范大学）；The School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；The School of Psychology and Cognitive Science, East China Normal University（华东师范大学心理与认知科学学院）
