Leveraging Skill-to-Skill Supervision for Knowledge Tracing
This addresses the challenge of cold starts and limited data in knowledge tracing for educational technology, though it is incremental as it builds on existing models.
The paper tackled the problem of knowledge tracing in intelligent tutoring systems by incorporating expert-labeled skill-to-skill relationships, and the result showed that their method outperformed a baseline Transformer model, with greater superiority in limited data scenarios.
Knowledge tracing plays a pivotal role in intelligent tutoring systems. This task aims to predict the probability of students answering correctly to specific questions. To do so, knowledge tracing systems should trace the knowledge state of the students by utilizing their problem-solving history and knowledge about the problems. Recent advances in knowledge tracing models have enabled better exploitation of problem solving history. However, knowledge about problems has not been studied, as well compared to students' answering histories. Knowledge tracing algorithms that incorporate knowledge directly are important to settings with limited data or cold starts. Therefore, we consider the problem of utilizing skill-to-skill relation to knowledge tracing. In this work, we introduce expert labeled skill-to-skill relationships. Moreover, we also provide novel methods to construct a knowledge-tracing model to leverage human experts' insight regarding relationships between skills. The results of an extensive experimental analysis show that our method outperformed a baseline Transformer model. Furthermore, we found that the extent of our model's superiority was greater in situations with limited data, which allows a smooth cold start of our model.