Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases.
Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases.
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DOI：
10.3389/frai.2022.994967
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发表时间：
2022
影响因子：
4
通讯作者：
Mavrikis, Manolis
中科院分区：
文献类型：
--
作者：
Gauthier, Andrea;Rizvi, Saman;Cukurova, Mutlu;Mavrikis, Manolis
关键词：
数据驱动的教育技术，如教育中的人工智能(AIED)系统、学习分析仪表板、开放式学习者模型和其他应用程序，通常都是为了帮助教师在课堂上做出更好的、循证的决策。在这类应用中解决数据和算法固有的性别、种族和其他偏见被视为增加这些系统责任的一种方式，并一直是该领域许多研究的重点，包括系统审查。然而，教师也可能持有隐性偏见。据我们所知，这项系统的文献综述是第一次调查数据驱动的技术影响了哪些类型的教师偏见，这些技术是如何或是否被设计来挑战这些偏见，以及哪些策略在促进公平的教学行为和决策方面最有效。根据PRISMA指南，对五个数据库的搜索返回n=359条记录，其中只有n=2个研究小组的研究被确定为相关。研究结果显示，几乎没有证据表明，数据驱动的技术在支持教师做出不那么有偏见的决策或促进公平的教学行为方面的能力得到了评估，尽管这种能力经常被用作在教育中使用数据驱动技术的核心论点之一。通过考察这两项研究以及在全文回顾期间不符合资格标准的相关研究，我们揭示了可以在减轻教师偏见方面发挥有效作用的方法，以及可能持续存在偏见的方法。最后，我们总结了未来研究的方向，这些研究应该寻求通过教师工具中的显式设计策略直接面对教师的偏见，以确保两种技术(包括数据、算法、模型等)的偏见的影响。而教师则被最小化了。我们提出了一个扩展的框架，通过动机、认知和技术去偏向策略来支持这一领域的未来研究和设计。
Data-driven technologies for education, such as artificial intelligence in education (AIEd) systems, learning analytics dashboards, open learner models, and other applications, are often created with an aspiration to help teachers make better, evidence-informed decisions in the classroom. Addressing gender, racial, and other biases inherent to data and algorithms in such applications is seen as a way to increase the responsibility of these systems and has been the focus of much of the research in the field, including systematic reviews. However, implicit biases can also be held by teachers. To the best of our knowledge, this systematic literature review is the first of its kind to investigate what kinds of teacher biases have been impacted by data-driven technologies, how or if these technologies were designed to challenge these biases, and which strategies were most effective at promoting equitable teaching behaviors and decision making. Following PRISMA guidelines, a search of five databases returned n = 359 records of which only n = 2 studies by a single research team were identified as relevant. The findings show that there is minimal evidence that data-driven technologies have been evaluated in their capacity for supporting teachers to make less biased decisions or promote equitable teaching behaviors, even though this capacity is often used as one of the core arguments for the use of data-driven technologies in education. By examining these two studies in conjunction with related studies that did not meet the eligibility criteria during the full-text review, we reveal the approaches that could play an effective role in mitigating teachers' biases, as well as ones that may perpetuate biases. We conclude by summarizing directions for future research that should seek to directly confront teachers' biases through explicit design strategies within teacher tools, to ensure that the impact of biases of both technology (including data, algorithms, models etc.) and teachers are minimized. We propose an extended framework to support future research and design in this area, through motivational, cognitive, and technological debiasing strategies.
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影响因子：
7.5
作者：
Bailey, Matthew D.;Michaels, David
通讯作者：
Michaels, David
DOI：
10.1007/s40593-015-0090-8
发表时间：
2016-03-01
影响因子：
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通讯作者：
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影响因子：
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影响因子：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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