An Analytical Approach Towards the Prediction of Undefined Parameters for the National Institutional Ranking Framework

• Published in: 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON) pp. 1 - 8
• Main Authors: Goyal, Anuva, Kritika, Vuppuluri, Prem Prakash, Babu, G.S. Sailesh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05-08-2023
• Subjects: Analytical models; Classification algorithms; Computational modeling; Data models; Decision trees; Function approximation; MSE; NIRF; Prediction algorithms; Predictive models; regression
• DOI: 10.1109/INDISCON58499.2023.10270448

The National Institutional Ranking Framework is a methodology adopted by the Ministry of Education, Government of India for ranking Higher Education Institutions (HEIs) in India. The NIRF ranks HEIs based on five key parameters - Teaching, Learning & Resources, Research and Professional Practice, Graduation Outcome, Outreach & Inclusivity, and Peer Perception. These parameters are each composed of multiple components that are calculated based on the data provided by the respective HEIs. While the NIRF provides well-defined functions for some of these components, others are undefined. Understanding how these functions are computed would assist stakeholders in more effective planning and decision-making for further improving their NIRF scores. This work studies various regression machine learning models and identifies the best-fitting model for approximating these undefined functions. All experimental work is performed on real NIRF data publicly available on the NIRF website.