评核及分级的组成部分

B. Eicher, David A. Joyner
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引用次数: 8

摘要

对大规模提供教育的主要批评之一是常规评估的陷阱,即学生评估可能会因自动化和规模而变得过度简化。在这项研究中,我们研究了计算机科学大规模研究生课程的学生在学位期间被评估的方式。该项目在短短几年内就扩大到1万多名学生,但授予的是传统的硕士学位,这为研究规模是通过向更常规的评估过渡还是通过将规模引入传统战略提供了机会。为此,我们调查了该项目提供的52门课程的教学大纲,以确定所使用的评估类型,并调查了教学团队评估这些评估的方法。我们将这些数据与历史入学数据合并,以获得他们在学位期间接受的各种评估和评估的总体摘要。我们最终发现,该项目的规模是通过扩大传统的评估和评估策略来管理的,因为大部分成绩是由人类教学团队根据项目和作业生成的,只有相对较小的一部分是由考试的自动评估生成的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Components of Assessments and Grading At Scale
One of the major criticisms of efforts towards offering education at scale has been the Trap of Routine Assessment, the risk that student assessment will suffer from becoming excessively simplified in service of automation and scale. In this research, we examine the ways that students in an at-scale graduate program in computer science were assessed during their degrees. The program in question has scaled to over 10,000 students in only a few years, but awards a traditional Master's degree, providing the opportunity to investigate whether scale was achieved by transitioning to more routine assessment or by bringing scale to traditional strategies. To do this, we investigate the syllabi of 52 classes offered through the program to identify the types of assessments used, and we survey teaching teams for their approaches to evaluating these assessments. We merge this data with historical enrollment data to gain an overall summary of the kinds of assessments and evaluations received during their degrees. We ultimately find the program's scale has been managed by scaling up traditional assessment and evaluation strategies as the majority of grades are generated by human teaching teams based on projects and homeworks, with a relatively smaller portion generated exclusively by automated evaluation of exams.
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