Departamento de Ingeniería Industrial, Universidad Católica del Norte, Antofagasta, Chile.
Direcciòn de Aseguramiento de la Calidad, Universidad de Antofagasta, Antofagasta, Chile.
Eval Program Plann. 2022 Jun;92:102058. doi: 10.1016/j.evalprogplan.2022.102058. Epub 2022 Feb 18.
Improving the productivity of higher education in every nation's economy is one of the main challenges faced in the current environment of competition and shrinking public funds. The effective use of resources is a crucial issue in Chile's higher education reform. Data envelopment analysis (DEA) has been widely applied to measure efficiency in universities, sometimes focused on teaching or research. Universities function as a complex production process in which teaching and research are linked in the internal structure, share some inputs, and continue across multiple periods. To deal with this complexity, we developed a new DEA model that incorporates network structures, carryover activities, and shared inputs in a dynamic approach. We applied this model to a set of 33 Chilean universities and compared the results to their rankings and accreditation status. Our proposed DEA model has advantages: (a) there are no subjective criteria, and (b) the model considers the internal structure of the university production model, as well as the inputs and outputs over time. The objectivity of the model allows us to evaluate overall efficiency in terms of the quantity and quality of teaching and research, removing exogenous criteria and judgments regarding the performance of higher education institutions. This new quantitative approach could generate disaggregated data to analyze efficiency improvement over time or serve as a benchmarking tool according to the universities' characteristics.
提高每个国家经济中高等教育的生产力是当前竞争和公共资金紧缩环境下面临的主要挑战之一。资源的有效利用是智利高等教育改革的一个关键问题。数据包络分析(DEA)已广泛应用于衡量大学的效率,有时侧重于教学或研究。大学作为一个复杂的生产过程运作,其中教学和研究在内部结构中相互关联,共享一些投入,并在多个时期持续进行。为了应对这种复杂性,我们开发了一种新的 DEA 模型,该模型在动态方法中纳入了网络结构、结转活动和共享投入。我们将该模型应用于一组 33 所智利大学,并将结果与其排名和认证状况进行了比较。我们提出的 DEA 模型具有以下优势:(a)没有主观标准,(b)该模型考虑了大学生产模型的内部结构,以及随时间推移的投入和产出。该模型的客观性使我们能够根据教学和研究的数量和质量评估整体效率,消除了对高等教育机构绩效的外部标准和判断。这种新的定量方法可以生成分解数据,以分析随时间推移的效率提高情况,也可以根据大学的特点作为基准工具。