Article: An empirical analysis of data mining technique to predict learning environment in higher education systems Journal: International Journal of System of Systems Engineering (IJSSE) 2026 Vol.16 No.4 pp.387 - 408 Abstract: Predicting and assessing learning situations improves higher education. This is where data mining (DM) can help derive inferences from enormous datasets. Multiple data mining methods uncover trends and patterns in educational datasets. These databases contain feedback, student performance, stakeholder participation, and demographics. ML algorithms predict student performance and identify classroom challenges. Course materials, learning analytics, academic performance, and student demographics are variables. The model analyses past data patterns and interactions in real-time to reveal complex correlations between multiple learning environment parameters. This enables customised support plans and proactive interventions for teachers and students. Teachers can create a more personalised and supportive learning environment by identifying at-risk kids and providing targeted support. The student success prediction model is assessed using accuracy, precision, recall, F-measure, and sensitivity variables. High questionnaire reliability, student satisfaction and academic achievement were also linearly related in the ANOVA study of stakeholders' satisfaction with higher education courses. Inderscience Publishers - linking academia, business and industry through research

Title: An empirical analysis of data mining technique to predict learning environment in higher education systems

Authors: K. Shilpa; K. Krishna Prasad

Addresses: Institute of Computer Science and Information Science, Srinivas University, Mukka, Mangalore, 574146, India ' Institute of Engineering and Technology, Srinivas University, Mukka, Mangalore, 574146, India

Abstract: Predicting and assessing learning situations improves higher education. This is where data mining (DM) can help derive inferences from enormous datasets. Multiple data mining methods uncover trends and patterns in educational datasets. These databases contain feedback, student performance, stakeholder participation, and demographics. ML algorithms predict student performance and identify classroom challenges. Course materials, learning analytics, academic performance, and student demographics are variables. The model analyses past data patterns and interactions in real-time to reveal complex correlations between multiple learning environment parameters. This enables customised support plans and proactive interventions for teachers and students. Teachers can create a more personalised and supportive learning environment by identifying at-risk kids and providing targeted support. The student success prediction model is assessed using accuracy, precision, recall, F-measure, and sensitivity variables. High questionnaire reliability, student satisfaction and academic achievement were also linearly related in the ANOVA study of stakeholders' satisfaction with higher education courses.

Keywords: higher education systems; data mining (DM); machine learning (ML); predictive models and T-test; complex meta-systems; data collection and preprocessing; information technology.

DOI: 10.1504/IJSSE.2026.156332

International Journal of System of Systems Engineering, 2026 Vol.16 No.4, pp.387 - 408

Received: 15 Dec 2023
Accepted: 21 Mar 2024
Published online: 13 Sep 2026 *

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Title: An empirical analysis of data mining technique to predict learning environment in higher education systems

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