A comparative study of supervised/unsupervised machine learning algorithms with feature selection approaches to predict student performance

The field of educational data mining (EDM) is one of the most growing fields that aims to improve the performance of students, academic staff, and overall institutional performance. The implementing process of data mining algorithms almost needs the feature selection process to find the most correlated features and improve the accuracy. In this paper, a comparative study is performed to study implementation of supervised/unsupervised algorithms in predicting the students' performance. The student's grade is classified using different fields of supervised and unsupervised algorithms such as decision trees, clustering, and neural networks. These algorithms were examined over the questionnaire dataset before/after feature selection to measure the effect of feature selection on the result accuracy. The results showed that the random forest decision tree outperformed other supervised/unsupervised algorithms. The results also showed that the performance evaluation of algorithms with the dataset after removing the less correlated attributes is enhanced for most of the algorithms.