适应性测试及其在医学教育中的应用述评

Steven A. Burr, Thomas Gale, Jolanta Kisielewska, Paul Millin, José M. Pêgo, Gergo Pinter, Iain M. Robinson, Daniel Zahra
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引用次数: 0

摘要

适应性测试有很长的历史,但在很大程度上没有得到承认。计算机测试的出现为将适应性测试纳入传统的学习计划创造了新的机会。相对而言,最近开发的软件可以根据难度或内容自动交付总结性评估。这两种类型的自适应测试都需要一个质量得到适当保证的大型题库。通过难度进行的自适应测试能够更可靠地评估单个候选人的表现,尽管这是以决策的透明度为代价的,并且需要单向导航。通过内容进行自适应测试可以减少补偿和有针对性的个人支持,从而保证所有所需结果的性能,尽管这是以发现学习为代价的。在这两种类型的适应性测试中,候选人会被呈现出不同的项目,这可能会被认为是不公平的。然而,当不同能力的考生收到相同的问题时,他们可能会收到太多他们可以轻松回答的问题,或者太多太难回答的问题。这两种情况都可能被认为是不公平的,因为他们都没有机会展示自己所知道的东西。根据难度进行调整可以解决这个问题。同样地,当每个人都面对相同的问题,但却回答错了不同的问题时,不提供个性化的支持和机会,让他们通过重访之前回答错的内容来展示自己在所有要求结果中的表现,也可能被认为是不公平的;按内容改编时要解决的问题。我们回顾了适应性测试发展背后的教育原理,并考虑了其固有的优势和局限性。我们探讨了不断追求改进的考试方法,以及软件如何促进个性化评估。我们强调这如何成为学习和改进课程的催化剂;促进学习者和教育者的共同参与。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A narrative review of adaptive testing and its application to medical education
Adaptive testing has a long but largely unrecognized history. The advent of computer-based testing has created new opportunities to incorporate adaptive testing into conventional programmes of study. Relatively recently software has been developed that can automate the delivery of summative assessments that adapt by difficulty or content. Both types of adaptive testing require a large item bank that has been suitably quality assured. Adaptive testing by difficulty enables more reliable evaluation of individual candidate performance, although at the expense of transparency in decision making, and requiring unidirectional navigation. Adaptive testing by content enables reduction in compensation and targeted individual support to enable assurance of performance in all the required outcomes, although at the expense of discovery learning. With both types of adaptive testing, candidates are presented a different set of items to each other, and there is the potential for that to be perceived as unfair. However, when candidates of different abilities receive the same items, they may receive too many they can answer with ease, or too many that are too difficult to answer. Both situations may be considered unfair as neither provides the opportunity to demonstrate what they know. Adapting by difficulty addresses this. Similarly, when everyone is presented with the same items, but answer different items incorrectly, not providing individualized support and opportunity to demonstrate performance in all the required outcomes by revisiting content previously answered incorrectly could also be considered unfair; a point addressed when adapting by content. We review the educational rationale behind the evolution of adaptive testing and consider its inherent strengths and limitations. We explore the continuous pursuit of improvement of examination methodology and how software can facilitate personalized assessment. We highlight how this can serve as a catalyst for learning and refinement of curricula; fostering engagement of learner and educator alike.
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