Canberra Match Normalization-Enhanced Decision Stump Classifier for Predicting Academic Performance in the Context of Smartphone Addiction

Abstract

Student academic performance (SAP) prediction is a key issue in education data analysis. Also, the assessment of students‟ performance is used to enhance the efficiency of educational institutions. With the development in educational institutions and modern technology, focusing on the academic performance prediction of the student based on access to the smartphone is the need of the hour. To improve the accuracy of student academic performance prediction, the Canberra Match Normalization-based Generalized Canonical Correlative Decision Stump Classifier (CMN-GCCDSC) is introduced. Initially, student data are collected from the dataset. After the data collection process, the proposed CMN-GCCDSC technique is applied in two phases namely data preprocessing and classification respectively. In the first phase, data preprocessing is carried out to eliminate duplicate data using the Canberra Match Data Normalization technique to minimize space and time consumption. In the second phase, data classification is performed with preprocessed output to classify student academic performance using a generalized canonical correlative decision stump classifier based on Smartphone addiction prediction. The generalized canonical correlation analysis is used for decision-making. Based on analysis, student academic performance is classified and results are obtained. An experimental assessment of the proposed CMN-GCCDSC technique and existing methods is carried out with metrics such as accuracy, sensitivity, specificity, space complexity, and time complexity. The CMN-GCCDSC technique is an effective solution that addresses the limitations of Genetic Algorithm (GA)-based decision tree classifiers. By combining the Decision Stump Classifier (DSC) approach with Generalized Canonical Correlation (GCC), the most important feature to consider for academic prediction among students can be selected, ultimately reducing the dimensionality of the dataset, and improving classifier performance. With higher accuracy rates achieved, this technique can help identify at-risk students early and discover hidden trends and patterns in student performance, leading to improved academic outcomes with additional support from institutions and faculties.