No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths
This work addresses a domain-specific problem for educational technology and student modeling, focusing on incremental improvements to handle sequence length variations in KT.
The paper tackles the problem of knowledge tracing (KT) methods struggling with sequences that are too long or too short, which can lead to high computational costs or overfitting. It proposes a Sequence-Flexible Knowledge Tracing (SFKT) model to address these issues, though no concrete results or numbers are provided in the abstract.
Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As sequences get longer, computational costs will increase exponentially. Therefore, KT methods usually truncate sequences to an acceptable length, which makes it difficult for models on online service systems to capture complete historical practice behaviors of students with too long sequences. Conversely, modeling students with short practice sequences using most KT methods may result in overfitting due to limited observation samples. To address the above limitations, we propose a model called Sequence-Flexible Knowledge Tracing (SFKT).