Dynamic Knowledge Modeling in Textbook-Based Learning
Various e-learning systems that provide electronic textbooks are gathering data on large numbers of student reading in- teractions. This data can potentially be used to model student knowledge acquisition. However, reading activity is often overlooked in canonical student modeling. Prior studies modeling learning from reading either estimate stu- dent knowledge at the end of all reading activities, or use quiz performance data with expert-crafted knowledge com- ponents (KCs). In this work, we demonstrate that the dy- namic modeling of student knowledge is feasible and that automatic text analysis can be applied to save expert ef- fort. We propose a data-driven approach for dynamic stu- dent modeling in textbook-based learning. We formulate the problem of modeling learning from reading as a reading- time prediction problem, reconstruct existing popular stu- dent models (such as Knowledge Tracing) and explore two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. We evalu- ate the proposed framework using a dataset collected from a Human-Computer Interaction course. Results show that our approach for reading modeling is plausible; the pro- posed Knowledge Tracing-based student model reliably out- performs baselines and the latent semantic-based approach can be a promising way to construct a KC model. Serving as the first step to model dynamic knowledge in textbook- based learning, our framework can be applied to a broader context of open-corpus personalized learning.
Publications
- Huang, Yun, Michael Yudelson, Shuguang Han, Daqing He, and Peter Brusilovsky. "A Framework for Dynamic Knowledge Modeling in Textbook-Based Learning." In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 141-150. ACM, 2016. (paper) (presentation)
