Seeing Beyond Expert Blind Spots: Online Learning Design for Scale and Quality

Seeing Beyond Expert Blind Spots: Online Learning Design for Scale and Quality
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超越专家盲点:规模和质量的在线学习设计

DOI:
10.1145/3411764.3445045
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发表时间:
2021
期刊:
CHI Conference on Human Factors in Computing Systems (CHI ’21
影响因子:
--
通讯作者:
Koedinger, Ken
Koedinger, Ken
中科院分区:
--
文献类型:
--
作者:
Wang, Xu;Rose, Carolyn;Koedinger, Ken

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最大化系统可伸缩性和质量有时是不一致的。这项工作提供了一个例子,表明在教学设计中可以同时实现可扩展性和质量,这与教师可能相信或期望的相反。我们将我们的研究定位于HCI方法的教育,并提供建议,以改善HCI教育界的主动学习。在设计学习和评估活动时,许多教师面临着使用开放式或封闭式活动的选择。封闭式活动,如多项选择题(mcq)可以自动反馈给学生。然而,一项针对22位HCI教授的调查显示,他们认为mcq不如开放式问题有价值,因此,为了实现可扩展性,使用mcq需要牺牲质量。一项针对178名学生的研究没有证据支持教师的观点。本文指出,在至少一些HCI领域中,使用mcq进行可扩展的指令和评估的希望大于关注。
Maximizing system scalability and quality are sometimes at odds. This work provides an example showing scalability and quality can be achieved at the same time in instructional design, contrary to what instructors may believe or expect. We situate our study in the education of HCI methods, and provide suggestions to improve active learning within the HCI education community. While designing learning and assessment activities, many instructors face the choice of using open-ended or close-ended activities. Close-ended activities such as multiple-choice questions (MCQs) enable automated feedback to students. However, a survey with 22 HCI professors revealed a belief that MCQs are less valuable than open-ended questions, and thus, using them entails making a quality sacrifice in order to achieve scalability. A study with 178 students produced no evidence to support the teacher belief. This paper indicates more promise than concern in using MCQs for scalable instruction and assessment in at least some HCI domains.
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