| Popis: |
The paper elaborates the conceptual framework for resource allocation model based on performance of Higher Educational Institutions (HEIs). It provides logical workflow to develop input-oriented and output-oriented prediction model to assign academic, financial, and research enhancing resources to the institutions. The input- and output-oriented parameters such as faculty student ratio, number of publications, number of citations, number of patents, capital and operational expenditure, student admission ratio, number of PhD scholars enrolled, and number of PhD scholars graduated would be considered for the conceptual framework. All parameters would be identified by applying supervised machine learning algorithm like correlation and regression analysis. The data envelopment analysis (DEA) would be used to measure relative efficiency of each institution. The unsupervised machine learning algorithm would be implemented to find out clusters of institutions based on their relative efficiency. Finally, the input-oriented and output-oriented predication model would be developed to allocate resources to the institutions. The conceptual framework is being developed as a hybrid model with combination of optimization techniques of operation research and machine learning algorithms. The prediction model plays a vital role to allocate resources to the HEIs based on their performance. It would become benchmarking and decision-making model in perspective with higher education sector in India for administrators, funding agencies, and policymakers. |
