Data Envelopment Analysis of Scientific Research Performance for Higher Vocational Colleges

Abstract

This research aims to evaluate the scientific research performance of higher vocational colleges in Sichuan within the evolving landscape of data science. The study pursues two primary objectives: firstly, to assess the scientific research performance of these institutions using advanced methodologies such as Data Envelopment Analysis (DEA) and the Malmquist index models; secondly, to explore the intricate relationship between scientific research inputs and efficiency through the lens of Rough Set theory. The dataset comprises scientific research inputs and outputs from 30 higher vocational colleges, spanning the years 2019 to 2021. The findings underscore an overall positive trend in scientific research performance across the higher vocational colleges under examination. However, a nuanced analysis using DEA and Malmquist index models identified that only five institutions demonstrated robust performance during the specified period. Furthermore, the study delves into the influential factors affecting scientific research efficiency, revealing that internal expenditure on scientific research funds and the presence of senior and above professional teachers play pivotal roles. These insights are gleaned through the application of Rough Set theory, providing a unique perspective within the realm of data science. In conclusion, the research recommends strategic interventions to improve research management and resource allocation, emphasizing their role in enhancing efficiency and mitigating disparities among higher vocational colleges in Sichuan, particularly in the context of data science. The study adopts a holistic approach, employing an integrated model that combines DEA, Malmquist, and Rough Set theory for a comprehensive evaluation of research performance within the evolving landscape of data science.