Enhancing Academic Excellence through Autonomy: A Data- Driven Analysis of NIRF Rankings in South Indian Higher Educational Institutions

Abstract

Published as part of the research "Impact on NIRF Rankings", this study examines how colleges in Andhra Pradesh, Tamil Nadu, Kerala, and Telangana are performing regarding their autonomous status under the National Institutional Ranking Framework (NIRF). Using a detailed dataset from 2016–2023, the study employs advanced statistical techniques, including descriptive statistics, paired t-tests, Wilcoxon signed-rank tests, and multiple regression models to uncover differences in rankings of NIRF before and after autonomy.

Time series analysis with trend lines and moving averages can identify significant trends or rank changes across the years. Using machine learning models, they predict the rankings in the future and determine which factors have been most influential for improving ranking based on some of the commonly used machine learnings like Random Forest or Support Vector Machines (SVM). Statistical data analysis revealed a direct co-variation witnessed in autonomous status with an uplifted NIRF ranking (Teaching, Learning & Resources - TLR and Research and Professional Practice — RPC).

The study also indicates that Autonomous colleges could perform better in NIRF rankings as they can have a more excellent hold on innovative initiatives, academic optimization, and higher-performance research outputs. Collectively, these insights are helpful to researchers in educational data mining and higher education management as they might inform policymakers or institutional leaders interested in using autonomy to enhance academic quality. This is an exploratory study, and future research should adopt a longitudinal perspective and expand it to investigate other performance indicators, such as student satisfaction or employability.

The study highlights the importance of autonomy in improving India's ranking in higher education through a strong methodological framework and sophisticated data analysis techniques adopted by this research work.