What Makes Learning Analytics Research Matter
What Makes Learning Analytics Research Matter
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是什么让学习分析研究变得如此重要
DOI：
10.18608/jla.2021.7647
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
X. Ochoa
中科院分区：
文献类型：
--
作者：
A. Wise;Simon Knight;X. Ochoa
新冠肺炎疫情带来的持续变化和挑战加剧了教育中长期存在的不平等，导致许多人质疑关于学习如何才能最好地造福所有学生的基本假设。对学习数据的渴求达到了前所未有的高度，有时对确保这个社区长期重视的原则--隐私、透明、公开、问责和公平--没有相应的关注。我们如何驾驭这一动态环境，对于学习分析的未来至关重要。通过过去八年来JLA出版物的视角来思考这个问题，我们强调了“以问题为中心”而不是“以工具为中心”研究的重要贡献。我们也重视(近端或远端)对闭合环路的最终目标的关注，将我们的分析结果联系起来，以改进它们所源自的学习。最后，我们认识到成熟周期的力量：使用生成的关于学习分析工具的实际使用和影响的信息来指导数据、分析和干预设计的新迭代。这类工作的一个关键背景因素是，我们发现并选择解决的学习问题从来不是一张白纸；它们嵌入了社会结构，反映了过去技术的影响；并有以前的推动因素、障碍和社会调解作用于它们。在这种情况下，我们必须提出尖锐的问题：我们现有系统的哪些部分对我们的工作构成挑战？它加强了哪些部分？这些影响，无论是有意还是无意，是否与我们的价值观和信仰一致？归根结底，学习分析之所以重要，是因为我们有能力为解决当前和长期存在的学习挑战做出贡献，不仅改进现有的系统，还考虑现有和可能的替代方案。这需要利用严格的分析方法，在解决学习的重要问题时纳入利益攸关方的声音，以促进跨背景的公平学习。这本杂志为讨论这些问题提供了一个中心空间，作为整个社区分享研究、实践、数据和工具的场所，整个学习分析周期都在追求这些目标。
The ongoing changes and challenges brought on by the COVID-19 pandemic have exacerbated long-standing inequities in education, leading many to question basic assumptions about how learning can best benefit all students. Thirst for data about learning is at an all-time high, sometimes without commensurate attention to ensuring principles this community has long valued: privacy, transparency, openness, accountability, and fairness. How we navigate this dynamic context is critical for the future of learning analytics. Thinking about the issue through the lens of JLA publications over the last eight years, we highlight the important contributions of “problem-centric” rather than “tool-centric” research. We also value attention (proximal or distal) to the eventual goal of closing the loop, connecting the results of our analyses back to improve the learning from which they were drawn. Finally, we recognize the power of cycles of maturation: using information generated about real-world uses and impacts of a learning analytics tool to guide new iterations of data, analysis, and intervention design. A critical element of context for such work is that the learning problems we identify and choose to work on are never blank slates; they embed societal structures, reflect the influence of past technologies; and have previous enablers, barriers and social mediation acting on them. In that context, we must ask the hard questions: What parts of existing systems is our work challenging? What parts is it reinforcing? Do these effects, intentional or not, align with our values and beliefs? In the end what makes learning analytics matter is our ability to contribute to progress on both immediate and long-standing challenges in learning, not only improving current systems, but also considering alternatives for what is and what could be. This requires including stakeholder voices in tackling important problems of learning with rigorous analytic approaches to promote equitable learning across contexts. This journal provides a central space for the discussion of such issues, acting as a venue for the whole community to share research, practice, data and tools across the learning analytics cycle in pursuit of these goals.