International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 17, No. 1, 8 Feb. 2025
Cover page and Table of Contents: PDF (size: 936KB)
Canberra Match Normalization-Enhanced Decision Stump Classifier for Predicting Academic Performance in the Context of Smartphone Addiction
PDF (936KB), PP.59-71
Views: 0 Downloads: 0
Author(s)
R. Ruth Belina
1,*
Lucia Agnes Beena
1
Charles Savarimuthu
2
Index Terms
Student Academic Performance Prediction, Data Preprocessing, Canberra Match Data Normalization Technique, Generalized Canonical Correlative Decision Stump Classifier, Decision Rule
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.
Cite This Paper
R. Ruth Belina, Lucia Agnes Beena, Charles Savarimuthu, "Canberra Match Normalization-Enhanced Decision Stump Classifier for Predicting Academic Performance in the Context of Smartphone Addiction", International Journal of Modern Education and Computer Science(IJMECS), Vol.17, No.1, pp. 59-71, 2025. DOI:10.5815/ijmecs.2025.01.05
Reference
[1]S. Hussain and M. Q. Khan, "Student-performulator: Predicting students’ academic performance at secondary and intermediate level using machine learning," Annals of data science, vol. 10, pp. 637-655, 2023.
[2]S. Rajendran, S. Chamundeswari, and A. A. Sinha, "Predicting the academic performance of middle-and high-school students using machine learning algorithms," Social Sciences & Humanities Open, vol. 6, p. 100357, 2022.
[3]R. R. Ahmed, F. Salman, S. A. Malik, D. Streimikiene, R. H. Soomro, and M. H. Pahi, "Smartphone use and academic performance of university students: a mediation and moderation analysis," Sustainability, vol. 12, p. 439, 2020.
[4]J. C. Wang, C.-Y. Hsieh, and S.-H. Kung, "The impact of smartphone use on learning effectiveness: A case study of primary school students," Education and Information Technologies, vol. 28, pp. 6287-6320, 2023.
[5]B. Rathakrishnan, S. S. Bikar Singh, M. R. Kamaluddin, A. Yahaya, M. A. Mohd Nasir, F. Ibrahim, et al., "Smartphone addiction and sleep quality on academic performance of university students: An exploratory research," International journal of environmental research and public health, vol. 18, p. 8291, 2021.
[6]S. Li and T. Liu, "Performance prediction for higher education students using deep learning," Complexity, vol. 2021, pp. 1-10, 2021.
[7]C. F. Rodríguez-Hernández, M. Musso, E. Kyndt, and E. Cascallar, "Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation," Computers and Education: Artificial Intelligence, vol. 2, p. 100018, 2021.
[8]B. K. Yousafzai, S. A. Khan, T. Rahman, I. Khan, I. Ullah, A. Ur Rehman, et al., "Student-performulator: Student academic performance using hybrid deep neural network," Sustainability, vol. 13, p. 9775, 2021.
[9]A. Nabil, M. Seyam, and A. Abou-Elfetouh, "Prediction of students’ academic performance based on courses’ grades using deep neural networks," IEEE Access, vol. 9, pp. 140731-140746, 2021.
[10]A. Alshanqiti and A. Namoun, "Predicting student performance and its influential factors using hybrid regression and multi-label classification," IEEE Access, vol. 8, pp. 203827-203844, 2020.
[11]L. Zhao, K. Chen, J. Song, X. Zhu, J. Sun, B. Caulfield, et al., "Academic performance prediction based on multisource, multifeature behavioral data," IEEE Access, vol. 9, pp. 5453-5465, 2020.
[12]E. Alhazmi and A. Sheneamer, "Early Predicting of Students Performance in Higher Education," IEEE Access, vol. 11, pp. 27579-27589, 2023.
[13]A. Rafique, M. S. Khan, M. H. Jamal, M. Tasadduq, F. Rustam, E. Lee, et al., "Integrating learning analytics and collaborative learning for improving student’s academic performance," IEEE Access, vol. 9, pp. 167812-167826, 2021.
[14]C. Cui, J. Zong, Y. Ma, X. Wang, L. Guo, M. Chen, et al., "Tri-Branch Convolutional Neural Networks for Top-k Focused Academic Performance Prediction," IEEE Transactions on Neural Networks and Learning Systems, 2022.
[15]G. Feng, M. Fan, and Y. Chen, "Analysis and prediction of students’ academic performance based on educational data mining," IEEE Access, vol. 10, pp. 19558-19571, 2022.
[16]H. Waheed, S.-U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani, and R. Nawaz, "Predicting academic performance of students from VLE big data using deep learning models," Computers in Human behavior, vol. 104, p. 106189, 2020.
[17]F. Giannakas, C. Troussas, I. Voyiatzis, and C. Sgouropoulou, "A deep learning classification framework for early prediction of team-based academic performance," Applied Soft Computing, vol. 106, p. 107355, 2021.
[18]X. Xu, J. Wang, H. Peng, and R. Wu, "Prediction of academic performance associated with internet usage behaviors using machine learning algorithms," Computers in Human Behavior, vol. 98, pp. 166-173, 2019.
[19]B. K. Francis and S. S. Babu, "Predicting academic performance of students using a hybrid data mining approach," Journal of medical systems, vol. 43, pp. 1-15, 2019.
[20]A. Akram, C. Fu, Y. Li, M. Y. Javed, R. Lin, Y. Jiang, et al., "Predicting students’ academic procrastination in blended learning course using homework submission data," Ieee Access, vol. 7, pp. 102487-102498, 2019.
[21]D. Sun, R. Luo, Q. Guo, J. Xie, H. Liu, S. Lyu, et al., "A University Student Performance Prediction Model and Experiment Based on Multi-Feature Fusion and Attention Mechanism," IEEE Access, 2023.
[22]D. Alboaneen, M. Almelihi, R. Alsubaie, R. Alghamdi, L. Alshehri, and R. Alharthi, "Development of a web-based prediction system for students’ academic performance," Data, vol. 7, p. 21, 2022.
[23]M. Kumar, G. Mehta, N. Nayar, and A. Sharma, "EMT: Ensemble meta-based tree model for predicting student performance in academics," in IOP Conference Series: Materials Science and Engineering, 2021, p. 012062.
[24]S. D. A. Bujang, A. Selamat, R. Ibrahim, O. Krejcar, E. Herrera-Viedma, H. Fujita, et al., "Multiclass prediction model for student grade prediction using machine learning," IEEE Access, vol. 9, pp. 95608-95621, 2021.
[25]N. A. Butt, Z. Mahmood, K. Shakeel, S. Alfarhood, M. Safran, and I. Ashraf, "Performance Prediction of Students in Higher Education Using Multi-Model Ensemble Approach," IEEE Access, vol. 11, pp. 136091-136108, 2023.