Title: Data Science in Education: Transforming Learning Processes, Educational Analytics, and Institutional Decision-Making
Authors: Dr Neha Paliwal (H.O.D
Computer Science
Shri Mahaveer College
Jaipur)
Volume: 10
Issue: 6
Pages: 44-60
Publication Date: 2026/06/28
Abstract:
The rapid growth of digital technologies has generated vast amounts of educational data, creating unprecedented opportunities to improve teaching, learning, assessment, and institutional management. Data Science (DS), which combines statistical analysis, machine learning, data mining, artificial intelligence, and big data technologies, has emerged as a transformative force in modern education. Educational institutions increasingly utilize data-driven approaches to monitor student performance, personalize learning experiences, predict academic outcomes, evaluate teaching effectiveness, and optimize curriculum development. Furthermore, emerging technologies such as Learning Analytics, Educational Data Mining (EDM), Internet of Things (IoT), Intelligent Tutoring Systems (ITS), and Artificial Intelligence (AI) have expanded the scope of educational data science and enabled the development of smart learning environments. This paper presents a comprehensive review of Data Science applications in education and explores its role in enhancing educational processes. Major areas discussed include social-emotional learning analytics, student monitoring systems, curriculum innovation, instructor performance evaluation, predictive modelling for student success, intelligent learning environments, automated assessment systems, and personalized learning pathways. A case study of the University of Florida's implementation of IBM analytics tools is examined to demonstrate the practical impact of data-driven decision-making in reducing student dropout rates. The paper concludes by highlighting current challenges, ethical considerations, and future research directions in Educational Data Science.