Parallelizing Automatic Temporal Cognitive Tool for Large-Scale Online Learning Analytics

Jiang, Tianrui; Wu, Wenjun; Pu, Yanjun

With the advent of Massive Online Open Courses (MOOCs), the data scale of student learning behavior has significantly increased. In order to analyze these datasets efficiently 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. We introduce Automatic Temporal Cognitive (ATC) model to describe temporal progress of online learners and evaluate their mastery of course knowledge. As a complex dynamic Bayesian network model, it often causes high computational overhead of training the ATC model via Probabilistic Programming tools. The time-consuming Monte Carlo sampling adopted by the mainstream implementations renders parameter fitting for the model a slow execution process. To address the issue, this paper proposes to transform the ATC model into the form of nonlinear Kalman filter and presents a new parallel ATC tool based on the Spark framework with the method of Unscented Kalman Filter (UKF). This tool improves the ATC model by using a parallel UKF method with the capability of automatically estimating the parameters in the whole sequential process. Experimental results demonstrate that this tool can achieve the fast execution speed and greatly improve the robustness of training parameters on different sizes of real educational data sets.