Learning Representations for Math Strategies using BERT
Abstract
Adapting to a student's problem solving strategy can lead to improved engagement and motivation. In this work, we develop an AI-based approach to analyze math learning strategies at scale. Specifically, we use a state-of-the-art AI model, namely, BERT to learn structure within strategies observed in large datasets. In particular, we consider the MATHia ITS and define strategies as sequences of steps that a student follows in solving the problem. We apply BERT pre-training to learn semantic representations of strategies from a workspace in MATHia that allows for different strategies. Further, we fine-tune these embeddings to train them on downstream tasks such as identifying a strategy and understanding drift in strategy. Our preliminary results are encouraging and demonstrate that BERT can uncover hidden structure in strategies and therefore is a promising direction to analyze large-scale math learning data.
Publication Title
L@S 2024 - Proceedings of the 11th ACM Conference on Learning @ Scale
Recommended Citation
Thapa Magar, A., Fancsali, S., Rus, V., Murphy, A., Ritter, S., & Venugopal, D. (2024). Learning Representations for Math Strategies using BERT. L@S 2024 - Proceedings of the 11th ACM Conference on Learning @ Scale, 514-518. https://doi.org/10.1145/3657604.3664711
