Investigating the Robustness of Knowledge Tracing Models in the Presence of Student Concept Drift
Investigating the Robustness of Knowledge Tracing Models in the Presence of Student Concept Drift
The article "Investigating the Robustness of Knowledge Tracing Models in the Presence of Student Concept Drift" examines how variations in student understanding and demographics, known as concept drift, impact the effectiveness of knowledge tracing models used in online learning platforms (F1, F3, F5). It challenges the common assumption that student behavior remains static over time, emphasizing the need to understand these behavioral changes to maintain model accuracy (F2, A1). The findings underscore the importance of adapting knowledge tracing models to account for such dynamic shifts, ensuring their continued reliability in evolving educational contexts (F4, F6). By addressing concept drift, these models can better reflect real-time student learning progress, thereby enhancing personalized education strategies. This research highlights a critical area for improvement in educational technology, advocating for more flexible and responsive modeling approaches.
