Architecting Analytics Across Multiple E-Learning Systems to Enhance Learning Design
Architecting Analytics Across Multiple E-Learning Systems to Enhance Learning Design
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DOI：
10.1109/tlt.2021.3072159
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发表时间：
2021-04
影响因子：
3.7
通讯作者：
Katerina Mangaroska;B. Vesin;V. Kostakos;Peter Brusilovsky;M. Giannakos
中科院分区：
文献类型：
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作者：
Katerina Mangaroska;B. Vesin;V. Kostakos;Peter Brusilovsky;M. Giannakos
随着分布式学习环境的广泛扩展，我们的学习方式变得比以往任何时候都更加多样化。这为整合不同的学习痕迹数据源提供了机会，可以为学习者的行为和学习过程的错综复杂提供更广泛的见解。我们认为，跨不同的电子学习系统组合分析可以潜在地衡量学习设计的有效性，并在分布式环境中最大限度地增加学习机会。作为迈向这一目标的一步，在这项研究中，我们考虑了如何将单一学习环境的背景扩大到一个整合了三个独立的电子学习系统的学习生态系统。我们提出了一个跨平台的架构，可以捕获、集成和存储来自学习生态系统的与学习相关的数据。为了展示跨平台架构的可行性和好处，我们使用回归和分类技术生成了带有分析的可解释模型，这些模型可供教师理解学习行为和感知教学方法对学习者表现的影响。结果表明，与来自单一学习系统的数据相比，合并三个电子学习系统的数据将分类精度提高了5倍。本文突出了跨平台学习分析的价值，并为创建新的跨系统数据驱动的研究实践提供了跳板。
With the wide expansion of distributed learning environments the way we learn became more diverse than ever. This poses an opportunity to incorporate different data sources of learning traces that can offer broader insights into learner behavior and the intricacies of the learning process. We argue that combining analytics across different e-learning systems can potentially measure the effectiveness of learning designs and maximize learning opportunities in distributed settings. As a step toward this goal, in this study, we considered how to broaden the context of a single learning environment into a learning ecosystem that integrates three separate e-learning systems. We present a cross-platform architecture that captures, integrates, and stores learning-related data from the learning ecosystem. To demonstrate the feasibility and the benefits of cross-platform architecture, we used regression and classification techniques to generate interpretable models with analytics that can be relevant for instructors in understanding learning behavior and sensemaking of the instructional method on learner performance. The results show that combining data across three e-learning systems improve the classification accuracy compared to data from a single learning system by a factor of 5. This article highlights the value of cross-platform learning analytics and presents a springboard for the creation of new cross-system data-driven research practices.