Learning analytics in outer space

Learning analytics (LA) has invested much effort in the investigation of students' behavior and performance within learning systems. This paper expands the influence of LA to students' behavior outside of learning systems and describes a novel machine learning model which automatically detects students' off-task behavior as students interact with a learning system, ASSISTments, based solely on log file data. We first operationalize social cognitive theory to introduce two new variables, affect states and problem set, both of which can be automatically derived from the logs, and can be considered to have a major influence on students' behavior. These two 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 to show the best performance compared with traditional modeling techniques. Implications of the study are then discussed.