Evaluating Bayesian Knowledge Tracing for Estimating Learner Proficiency and Guiding Learner Behavior

Abstract

Open navigation online learning systems allow learners to choose the next learning activity. These systems can be instrumented to provide learners with feedback to help them choose the next learning activity. One type of feedback is providing an estimate of the learner's current skill proficiency. A learner can then choose to skip the remaining learning activities for that skill after achieving proficiency in that skill. In this paper, we investigate whether predicting proficiency and communicating it to learners can save time for learners within a course. We evaluate the accuracy of the BKT based proficiency pre- diction framework for learner's proficiency prediction which considers one attempt per question. We extend the proficiency prediction framework to include multiple attempts at individual questions and show that it is more accurate in proficiency prediction than BKT based proficiency prediction framework. We discuss the potential implications of attempt enhanced framework on the learners' behavior for open navigation on- line learning systems.