Principled Transformers for Predictive Performance in Knowledge Tracing

Knowledge tracing aims to model students' knowledge and abilities over time, which is crucial for intelligent tutoring systems. In this paper, we propose a straightforward model class, knowledge tracing set transformers (KTSTs), specifically addressing predictive performance in knowledge tracing tasks. KTSTs closely follow prominent transformer architectures and use an intuitive set-based representation for student interactions. We introduce learnable ALiBi, which simplifies and improves upon a prevalent attention mechanism in knowledge tracing, and MHSA aggregation, which readily allows incorporating an arbitrary number of additional, potentially more complex features per student interaction. We highlight and discuss flaws present in related approaches, which are overly complex and, in part, based on suboptimal design choices. We validate our design choices for KTSTs in experiments with real-world data and simulated learning sequences. Overall, we address lessons learned and propose a straightforward model that relies on best practices and establishes a new state-of-the-art on standardized benchmark datasets. Ultimately, KTSTs may serve as a simple but effective base model class for future research in knowledge tracing and intelligent tutoring systems. Code is available at https://github.com/kainbr/kt_set_transformers.