Parallelizing Bayesian Knowledge Tracing Tool For Large-scale Online Learning Analytics

Abstract

With the advent of Massive Online Open Courses (MOOCs), the data scale of student learning behavior and knowledge mastery has significantly increased. In order to effectively and efficiently analyze these datasets and present on-the-fly intelligent tutoring to online learners, it is necessary to improve existing learning analytics tools in a parallel and automatic way. One of the most common tools is Bayesian Knowledge Tracing (BKT) that can model temporal progress of online learners and evaluate their mastery of course knowledge. Current implementation of BKT is mostly based on single machine, which leads to slow execution performance during its Expectation Maximization(EM) algorithm for parameter fitting. Although there are a few parallel implementations for EM algorithm, they don’t support automatic initial BKT parameter tuning to ensure the correct convergence of the EM iteration. Therefore, this paper presents a new parallel BKT open source tool based on the Spark computational framework with the method of automatic tuning of initial parameters. This tool improves traditional knowledge tracing systems using a parallel EM algorithm with the capabilty of automatically choosing initial parameters. Experimental result demonstrates that our tool can achieve fast execution speed and greatly improve the accuracy of training parameters on both different sizes of simulated data and real educational data sets.