Modeling Students’ Academic Performance Based on Their Interactions in an Online Learning Environment

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