Designing an Off-Task Detector in Distance Learning Systems

• In Event: DDL - Digital Tools

Short Description

This paper describes a novel machine learning model which automatically detects students’ off-task behavior as students are interacting with a learning system, ASSISTments, based solely on log file data. We first operationalize social cognitive theory to introduce new variables. These new variables further work as the feature vector data for a K-means clustering algorithm in order to quantify students’ different behavioral characteristics. This quantified variable representing student behavior type expands the feature space and contributes to the improvement of the various model performance compared with only time- and performance-related features. In addition, an advanced Hidden Naïve Bayes (HNB) algorithm is coded for off-task behavior detection and show the best performance compared with traditional modeling techniques.

Authors

• Wanli Xing, University of Missouri-Columbia

• Sean Goggins, University of Missouri-Columbia