研讨会提案:大规模的教育A/B测试

Steven Ritter, N. Heffernan, J. Williams, Burr Settles, Phillip J. Grimaldi, Derek J. Lomas
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摘要

学习工程这一新兴学科专注于将学习科学作为改善教育成果基础的工具和过程落实到位[3]。学习工程的一个重要组成部分是提高教育软件的有效性。在许多软件领域,A/B测试已经成为实现软件目标的重要技术[1]。许多大公司(亚马逊、谷歌、Facebook等)都进行了数千次AB测试,并出席了数字实验年会上(CODE)的会议,但该会议的范围太广,无法解决EdTech平台特有的AB测试问题。我们认为有必要解决在教育环境中运行大规模a /B测试的问题,因为a /B测试的使用落后于其他行业。本次研讨会将探讨教育背景下的A/B测试与其他领域的不同之处,并提出克服当前挑战的建议,使这种方法成为学习工程师工具箱中更有用的工具。预计将处理的问题包括:
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Workshop Proposal: Educational A/B Testing at Scale
The emerging discipline of Learning Engineering is focused on putting into place tools and processes that use the science of learning as a basis for improving educational outcomes [3]. An important part of Learning Engineering focuses on improving the effectiveness of educational software. In many software domains, A/B testing has become a prominent technique to achieve the software’s goals [1]. Many large companies (Amazon, Google, Facebook, etc.) run thousands of AB tests and present at the Annual Conference on Digital Experimentation (CODE), but that venue is too broad to address AB testing issues specific to EdTech platforms. We see a need to address issues with running large-scale A/B tests within the educational context, where the use of A/B testing lags other industries. This workshop will explore ways in which A/B testing in educational contexts differs from other domains and proposals to overcome current challenges so that this approach can become a more useful tool in the learning engineer’s toolbox. Issues to be addressed are expected to include:
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Trust, Sustainability and [email protected] L@S'22: Ninth ACM Conference on Learning @ Scale, New York City, NY, USA, June 1 - 3, 2022 L@S'21: Eighth ACM Conference on Learning @ Scale, Virtual Event, Germany, June 22-25, 2021 Leveraging Book Indexes for Automatic Extraction of Concepts in MOOCs Evaluating Bayesian Knowledge Tracing for Estimating Learner Proficiency and Guiding Learner Behavior
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