Article: Design and development of a student information management platform based on support vector machines and data mining Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.92 pp.111 - 130 Abstract: This paper proposes a student information management platform that integrates support vector machine (SVM) modelling with data mining to improve academic early warning and personalised guidance. The platform adopts a four-layer architecture covering data, processing, application, and presentation functions. Multi-source student data, including academic records, behavioural information, and consumption data, are cleaned through missing value imputation, outlier correction, and feature selection to form model-ready datasets. An SVM model with an RBF kernel is developed for predicting course-failure risk and performance fluctuations, with parameters optimised by grid search (C = 10, γ = 0.1). Using 5,000 undergraduate records, the model is compared with decision tree, KNN, and logistic regression methods. Results show that the SVM model achieves 89.6% accuracy, outperforming decision tree and KNN models. The platform also maintains acceptable response and prediction times under 500 concurrent users. Inderscience Publishers - linking academia, business and industry through research

Title: Design and development of a student information management platform based on support vector machines and data mining

Authors: Yuzhen Jin

Addresses: School of Civil Engineering and Architecture, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang, 322100, China

Abstract: This paper proposes a student information management platform that integrates support vector machine (SVM) modelling with data mining to improve academic early warning and personalised guidance. The platform adopts a four-layer architecture covering data, processing, application, and presentation functions. Multi-source student data, including academic records, behavioural information, and consumption data, are cleaned through missing value imputation, outlier correction, and feature selection to form model-ready datasets. An SVM model with an RBF kernel is developed for predicting course-failure risk and performance fluctuations, with parameters optimised by grid search (C = 10, γ = 0.1). Using 5,000 undergraduate records, the model is compared with decision tree, KNN, and logistic regression methods. Results show that the SVM model achieves 89.6% accuracy, outperforming decision tree and KNN models. The platform also maintains acceptable response and prediction times under 500 concurrent users.

Keywords: intelligent management of student information; support vector machine; SVM; educational data mining; EDM; academic risk early warning; platform design.

DOI: 10.1504/IJICT.2026.155942

International Journal of Information and Communication Technology, 2026 Vol.27 No.92, pp.111 - 130

Received: 11 Apr 2026
Accepted: 13 May 2026
Published online: 26 Aug 2026 *

Title: Design and development of a student information management platform based on support vector machines and data mining

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