Comparative Analysis of Performance Indicators To Improve The Quality in Indian Educational Institutions Using GRD Ensemble Methods

The Indian government uses a mechanism called the National Institutional Ranking Framework (NIRF) to rate those institutions of higher education. The NIRF uses five metrics, including research and development, teaching and learning-resources, graduation-outcome, outreach and inclusivity, and perception, to rank institutions. However, there is ongoing debate about the comparability and accuracy of the NIRF metrics. This study aims to compare the NIRF metrics used for ranking higher education institutions in India. In this study, GRD stands for machine learning algorithms including Gradient Boosting, Random Forest, and Decision Tree are used together with a mixed-methods strategy that combines qualitative and quantitative research techniques. The findings of the study suggest that while the NIRF metrics have some strengths and limitations, they provide a valuable tool for ranking higher education institutions in India. The study’s results will be useful for stakeholders in the education sector, including policy makers, higher education institutions, and stakeholders, gain a better understanding of the NIRF measures and their applicability for ranking higher education institutions in India. Finding the key performance indicators (KPIs) that can be used to assess and improve the calibre of instruction in Indian educational institutions. The study also emphasises the necessity of continuing data gathering and analysis to maintain the usefulness and efficacy of these KPIs. In the conclusion, this study delivers insightful information about how data analysis contributes to raising educational standards in India and offers actionable advice for institutions wishing to improve.