Influence of course characteristics, student characteristics, and behavior in learning management systems on student performance

The use of learning management systems (LMS) in education make it possible to track students’ online behavior. This data can be used for educational data mining and learning analytics, for example, by predicting student performance. Although LMS data might contain useful predictors, course characteristics and student characteristics have shown to influence student performance as well. However, these different sets of features are rarely combined or compared. Therefore, in the current study we classify student performance using information from course characteristics, student characteristics, past performance, and LMS data. Three classifiers (decision tree, rule-based, and SVM) are trained and compared with the majority class baseline. Overall, SVM is the best classifier to identify pass/fail for a student in a course. However, for more interpretable results, the decision tree or the rule-based algorithm with course characteristics, student characteristics, and midterm data are good second bests. Additionally, it is shown that the different feature sets all have a positive influence on predicting pass/fail. In particular, student characteristics and the midterm grade have a large influence. Compared to these feature sets, LMS data seems less important. Yet, a more fine-grainedanalysis of the specific metrics found in the learning management system may still yield useful information.