An Adaptive Feature Selection Algorithm for Student Performance Prediction

Abstract

Educational Data Mining (EDM) is used to ameliorate the teaching and learning pro- cess by analyzing and classifying data that can be applied to predict the students’ academic performance, and students’ dropout rate, as well as instructors’ performance. The predic- tion of student performance is complicated by the vast and diverse range of variables from academic records to behavioral and health metrics. In this thesis book, we have intro- duced a new Adaptive Feature Selection Algorithm (AFSA) by amalgamating an ensemble approach for initial feature ranking with normalized mean ranking from five distinct meth- ods to enhance robustness. The proposed method iteratively selects the best features by adjusting its threshold based on each feature’s rank to ensure significant contributions to model accuracy and also effectively reduces dataset complexity. We have tested the performance of the proposed feature selection algorithm using five machine learning clas- sifiers: Logistic Regression (LR), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Na ̈ıve Bayes (NB) classifier, and Decision Tree (DT) classifier on four student performance datasets. The experimental results highlight the proposed method signifi- cantly decreases feature count by an average feature reduction factor of 5.7, significantly streamlining datasets while maintaining competitive cross-validation accuracy, marking it as a valuable tool in the field of educational data analytics.