Data Analytics and Machine Learning Applications for Enhancing Strategic Decision-Making in Higher Education

DOI

10.2991/978-94-6239-697-5_18How to use a DOI?

Keywords

Data-Driven Decision-Making; Higher Education; Machine Learning; Classification; Prediction; Strategic Planning

Abstract

In the age of digital revolution, higher education institutions are increasingly accepting data-driven approaches to enhance strategic decision-making. This study explores the application of machine learning (ML) classification and prediction models to support institutional planning, student performance analysis, and resource optimization. The dataset comprises academic planning, student performance, faculty development, research and institutional development, decision-making aspects. These aspects are considered through various machine learning algorithms. Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machines (SVM) and Ensemble Learning algorithm were implemented and evaluated for accuracy and interpretability. Various objectives are achieved through machine learning applications in educational strategic decision-making. The results reveal that Ensemble Learning and Decision Tree models achieved the highest prediction accuracy regarding decision making by providing actionable insights for academic interventions, faculty development, research and institutional development and institutional engagement. Also, the predictive analytics framework developed in this study demonstrates how machine learning can inform evidence-based strategies in areas such as student retention, program evaluation, faculty development, and institutional effectiveness. The findings highlight the potential of machine learning-driven decision support systems to transform higher education management from intuition-based to evidence-oriented strategic planning. This research contributes as a machine learning application in education and offers a scalable outline for incorporating predictive analytics into institutional educational decision-making processes.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bharati Kawade
PY  - 2026
DA  - 2026/06/04
TI  - Data Analytics and Machine Learning Applications for Enhancing Strategic Decision-Making in Higher Education
BT  - Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
PB  - Atlantis Press
SP  - 210
EP  - 219
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-697-5_18
DO  - 10.2991/978-94-6239-697-5_18
ID  - Kawade2026
ER  -