Abstract

The aim of this study is to model students' academic performance based on their interactions in an online learning environment. The dataset includes 10 input attributes extracted from students' learning interaction data. As an output (class) variable, the final grades obtained from their Computer Hardware course were used. The modeling performance of three different classification algorithms were tested (naïve Bayes classifier, classification tree and CN2 rules) on the dataset. All analyses were performed using the Orange data mining tool, and the models were evaluated using ten-fold cross-validation. The results of analysis were presented as a confusion matrix, a decision tree, and if-then rules. The predictive performance of the algorithms was also tested and compared using the classification accuracy (CA), and area under the ROC Curve (AUC) metrics. The experimental results indicate that the naïve Bayes algorithm outperforms other classification algorithms when compared using the CA and AUC metrics. The naïve Bayes algorithm correctly classified 75.4% of the students according to their grade for the course (Fail, Pass, and Good). The classification model also accurately predicted 81.5% of the students who failed, and 91.8% of the students who passed the course. On the other hand, the classification tree and the CN2 algorithms generated models which can be used with confidence in decision making processes by non-expert data mining users.

Key words: academic performance modeling, final grade prediction, classification, educational data mining, learning analytics.