Wong, C. S. Y., & Ramasamy, S. (2024). Architectural Adaptation and Regularization of Attention Networks for Incremental Knowledge Tracing. In (Editor), Proceedings of the 14th Learning Analytics and Knowledge Conference. https://doi.org/10.1145/3636555.3636859
Abstract:
EdTech platforms continuously refresh their database with new questions and concepts with evolving course syllabus. The state-of-the-art knowledge tracing models are unable to adapt to these changes, as the size of the question embedding layers is typically fixed. In this work, we propose an incremental learning algorithm for knowledge tracing that is capable of adapting itself to growing pool of concepts and questions, through its architectural adaptation and regularization strategies. The algorithm, referred as, "Architectural adaptation and Regularization of Attention network for Incremental Knowledge Tracing (ARAIKT)", is capable of adapting the embeddings with increasing concepts and question bank, while preserving representations of the previous concepts and question banks. Furthermore, they are robust to distributional drifts in the data, and are capable of preserving privacy of data across study centers and EdTech platforms. We demonstrate the effectiveness of the ARAIKT by evaluating its performance on subsets of study centers/academic years within ASSISTment2009 and ASSISTment2017 data sets, respectively.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
There was no specific funding for the research done