The underlying signals of efficiency in European universities: a combined efficiency and machine learning approach
本研究结合数据包络分析和机器学习特征选择方法，分析欧洲高等教育机构效率的关键决定因素，发现学生资助、博士项目强度和国际化流动对效率有重要影响，为政策制定提供参考。
This study explores the critical determinants of efficiency in European Higher Education Institutions (HEIs). The contemporary landscape presents a wide range of challenges and opportunities, necessitating unprecedented levels of efficiency. Our primary objective is to unravel the fundamental elements that drive the efficiency of European HEIs. To achieve this, we employ a novel methodological approach, integrating Data Envelopment Analysis (DEA) with a Machine Learning technique, specifically Feature Selection. This combination sheds new light on areas previously less explored in DEA, including the intricate interplay among variables. Our empirical investigation contributes to the academic discourse by presenting a comprehensive, multi-country analysis from a multifaceted viewpoint. We assess the efficiency levels of European universities, highlighting the influence of factors such as student fee funding, Ph.D. program intensity, and international mobility on achieving high efficiency scores. The findings of this study have some implications for policy-making, suggesting strategies to enhance the efficiency levels of HEIs.