Data-driven Decisions of Higher Education Instructors in an Era of a Global Pandemic

M. Usher, A. Hershkovitz
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Abstract

The impact of the COVID-19 pandemic on the higher education sector has been overwhelming, with emergency responses that have affected decision-making processes. Yet, our understanding of higher education instructors’ perspectives regarding the process of data-driven decisions, especially in times of emergency, is still limited. We aimed at characterizing the types of data-driven decisions that higher education instructors have made in their courses. This was done while asking the instructors to reflect upon a face-to-face (F2F) course that was suddenly shifted to emergency remote teaching (ERT), due to the COVID-19 pandemic outbreak. Taking a qualitative approach, data were collected via an open-ended online questionnaire distributed among 109 higher education instructors from different countries. The findings suggest that the instructors mentioned a wider range of data sources, and a wider range of data-driven decisions while referring to the ERT mode, compared with their F2F instruction. In F2F teaching, the instructors mostly provided students with real-time educational assistance. In ERT, the instructors mostly adjusted the course requirements, promoted collaboration among students, and offered them social and emotional support.
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全球大流行时代高等教育教师的数据驱动决策
新冠肺炎疫情对高等教育部门的影响是巨大的,应急响应影响了决策过程。然而,我们对高等教育讲师关于数据驱动决策过程的观点的理解,尤其是在紧急情况下,仍然有限。我们旨在描述高等教育讲师在课程中做出的数据驱动决策的类型。这是在要求教师反思面对面(F2F)课程时完成的,由于新冠肺炎大流行爆发,该课程突然改为紧急远程教学(ERT)。采用定性方法,通过向来自不同国家的109名高等教育讲师分发的开放式在线问卷收集数据。研究结果表明,与他们的F2F教学相比,教师在提到ERT模式时提到了更广泛的数据源和更广泛的基于数据的决策。在F2F教学中,教师大多为学生提供实时的教育帮助。在ERT中,教师主要调整课程要求,促进学生之间的合作,并为他们提供社会和情感支持。
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