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.

中文翻译：

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跨多个电子学习系统构建分析以增强学习设计


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更加多样化。这提供了整合不同学习轨迹数据源的机会，可以为学习者行为和学习过程的复杂性提供更广泛的见解。我们认为，结合不同电子学习系统的分析可以潜在地衡量学习设计的有效性并最大化分布式环境中的学习机会。作为实现这一目标的一步，在本研究中，我们考虑了如何将单一学习环境的背景扩展到集成三个独立的电子学习系统的学习生态系统。我们提出了一种跨平台架构，可以捕获、集成和存储来自学习生态系统的学习相关数据。为了证明跨平台架构的可行性和好处，我们使用回归和分类技术来生成具有分析功能的可解释模型，这些模型可以帮助教师理解学习行为和对学习者表现的教学方法进行意义建构。结果表明，与来自单个学习系统的数据相比，结合三个电子学习系统的数据可以提高分类准确性 5 倍。本文强调了跨平台学习分析的价值，并为创建新的学习分析提供了一个跳板。跨系统数据驱动的研究实践。