Predicting Grades by Principal Component Analysis: A Data Mining Approach to Learning Analyics

In this paper we introduce three main features extracted from Moodle logs in order to be uses a possible means to predict future student grades. We discuss the statistical analysis on these features and show how they cannot be applied isolatedly to model our data. We then apply them as a whole and use principal component analysis to derive a decision tree based on the features. With derived tree we are able to predict grades in three intervals, namely to predict failures. Our proposed analysis methodology can be incorporated in an LMS and be used during a course. As the course unfolds, the system can to trigger alarms regarding possible failure situations.