Interpretable Personalized Knowledge Tracing and Next Learning Activity Recommendation
Jinjin Zhao, Shreyansh Bhatt, Candace Thille, Dawn Zimmaro, Neelesh Gattani
Abstract
Online learning systems that provide actionable and personalized guidance can help learners make better decisions during learning. Bayesian Knowledge Tracing (BKT) extensions and deep learning based approaches have demonstrated improved mastery prediction accuracy compared to the basic BKT model; however, neither set of models provides actionable guidance on learning activities beyond mastery prediction. We propose a novel framework for personalized knowledge tracing with attention mechanism. Our proposed framework incorporates auxiliary learner attributes into knowledge tracing and interprets mastery prediction with the learning attributes. The proposed approach can also provide personalized next best learning activity recommendations. We demonstrate that the accuracy of the proposed approach in mastery prediction is slightly higher compared to deep learning based approaches and that the proposed approach can provide personalized next best learning activity recommendation.