Advanced Knowledge Tracing: Incorporating Process Data and Curricula Information via an Attention-Based Framework for Accuracy and Interpretability

Knowledge tracing aims to model and predict students’ knowledge states during learning activities.
Traditional methods like Bayesian Knowledge Tracing (BKT) and logistic regression have limitations
in granularity and performance, while deep knowledge tracing (DKT) models often suffer from lacking
transparency. This paper proposes a Transformer-based framework that emphasizes both accuracy
and interpretability. It captures the relationship between student behaviors and learning outcomes considering
the associations between exam and exercise problems. We participated in the EDM Cup 2023
Contest using the proposed framework and achieved first place on the task of predicting students’ performance
on end-of-unit test problems using clickstream data from previous assignments. Furthermore,
the framework provides meaningful insights by analyzing user actions and visualizing attention weight
matrices. These insights enable targeted interventions and personalized support, enhancing online learning
experiences. We have uploaded our code, saved models, and predictions to an OSF repository:
https://osf.io/mdpzc/.