通过众包自适应学习系统促进和支持评价性判断的实证研究

Hassan Khosravi, George Gyamii, Barbara E. Hanna, J. Lodge
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引用次数: 13

摘要

在高等教育文献中,学生培养对自己和他人的工作质量做出准确判断的能力的价值已得到广泛认可。然而,尽管认识到这一点,却很少注意开发既能促进评价判断又能支持对其增长进行实证研究的工具和战略。本文提供了如何使用教育技术来填补这一空白的演示。特别是,我们介绍了自适应学习系统RiPPLE,并描述了它是如何(1)通过文献中建议的策略,如使用规则、范例和同行评审,在大班环境中发展评估性判断的;(2)以低成本进行大型实证研究,以确定这些策略的效应大小。介绍了一个案例研究,展示了RiPPLE如何在特定环境中用于实现这些目标。
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Fostering and supporting empirical research on evaluative judgement via a crowdsourced adaptive learning system
The value of students developing the capacity to make accurate judgements about the quality of their work and that of others has been widely recognised in higher education literature. However, despite this recognition, little attention has been paid to the development of tools and strategies with the potential both to foster evaluative judgement and to support empirical research into its growth. This paper provides a demonstration of how educational technologies may be used to fill this gap. In particular, we introduce the adaptive learning system RiPPLE and describe how it aims to (1) develop evaluative judgement in large-class settings through suggested strategies from the literature such as the use of rubrics, exemplars and peer review and (2) enable large empirical studies at low cost to determine the effect-size of such strategies. A case study demonstrating how RiPPLE has been used to achieve these goals in a specific context is presented.
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