A Novel Approach for Knowledge State Representation and Prediction

Abstract

Online learning systems with open navigation allow learners to select the next learning activity in order to achieve desired mastery. To help learners make an informed choice regarding the next learning activity, we propose to represent and communicate the learner's knowledge state as the average success rate in the course for each skill, rather than as the probability of correctly answering the next question. We first show that we can accurately estimate the proposed knowledge state. We then show that the proposed attention-based model to estimate the knowledge state requires fewer parameters, provides actionable information to the learners, and achieves equivalent or better accuracy compared to RNN (Recurrent Neural Network) based models.