Efficiency analysis of engineering classes: A DEA approach encompassing active learning and expositive classes towards quality education

IF 4.9 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Paulo Nocera Alves Junior , Paul Leger , Isotilia Costa Melo
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Abstract

The science, technology, engineering, and mathematics (STEM) education research delves into the core of sustainable development goals (SDGs), including the pillars of quality education (SDG4), robust economic growth (SDG8), and diminished inequalities (SDG10). These pursuits stand as keystones in sculpting inclusive societies and bridging societal gaps. While previous studies utilising data envelopment analysis (DEA) have explored educational performance mainly from a macro-perspective, there is a lack of micro-perspective investigation. Our study aims to fill this gap by proposing a DEA approach to assess the relative efficiency of engineering classes. We analysed 70 classes covering 38 subjects in the first semester of 2022 at a South American school. Methodologically, we employed the slack-based measure (SBM) model under the benefit of doubt (BoD) condition. Unlike prior research, we analysed classes' relative performance considering different pedagogical approaches - 11 active-learning classes (15.7 %) and 59 passive-learning classes (84.3 %). Our results showed that 18 classes were efficient (25.7 %). Active classes were more efficient, but few subjects maintained similar efficiencies for all classes. Moreover, efficient classes were concentrated in the last two years prior to graduation (57.9 %). This may represent an additional barrier for low-income students, who tend to drop out in the first years. The findings support several improvement recommendations, such as integrating digital technologies, boosting active learning opportunities, and bolstering classes in foundational subjects. Also, implications for researchers, decision- and policy-makers are discussed. Our approach can be replicated in diverse educational contexts, enabling the identification of strengths and weaknesses for more efficient educational management.

工程学课堂的效率分析:一种包含主动学习和论述式课堂的 DEA 方法,以实现优质教育
科学、技术、工程和数学(STEM)教育研究深入到可持续发展目标(SDGs)的核心,包括优质教育(SDG4)、强劲的经济增长(SDG8)和减少不平等(SDG10)等支柱。这些目标是构建全纳社会和缩小社会差距的基石。以往利用数据包络分析(DEA)的研究主要从宏观角度探讨教育绩效,但缺乏微观角度的调查。我们的研究旨在通过提出一种 DEA 方法来评估工科课堂的相对效率,从而填补这一空白。我们分析了南美一所学校 2022 年第一学期的 70 个班级,涵盖 38 个科目。在方法上,我们采用了基于松弛度量(SBM)的模型,并以 "疑点利益"(BoD)为条件。与之前的研究不同,我们分析了不同教学方法下班级的相对绩效--11 个主动学习型班级(15.7%)和 59 个被动学习型班级(84.3%)。结果显示,18 个班级的效率较高(25.7%)。主动学习型班级的效率更高,但很少有科目在所有班级都保持相似的效率。此外,高效课堂主要集中在毕业前的最后两年(57.9%)。这对低收入学生来说可能是一个额外的障碍,因为他们往往在前几年辍学。研究结果支持几项改进建议,如整合数字技术、增加主动学习机会、加强基础学科课程等。此外,还讨论了对研究人员、决策者和政策制定者的影响。我们的方法可以在不同的教育环境中推广,从而找出优缺点,提高教育管理效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental Science & Policy
Environmental Science & Policy 环境科学-环境科学
CiteScore
10.90
自引率
8.30%
发文量
332
审稿时长
68 days
期刊介绍: Environmental Science & Policy promotes communication among government, business and industry, academia, and non-governmental organisations who are instrumental in the solution of environmental problems. It also seeks to advance interdisciplinary research of policy relevance on environmental issues such as climate change, biodiversity, environmental pollution and wastes, renewable and non-renewable natural resources, sustainability, and the interactions among these issues. The journal emphasises the linkages between these environmental issues and social and economic issues such as production, transport, consumption, growth, demographic changes, well-being, and health. However, the subject coverage will not be restricted to these issues and the introduction of new dimensions will be encouraged.
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