Social learning analytics: five approaches

Rebecca Ferguson, S. B. Shum
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引用次数: 287

Abstract

This paper proposes that Social Learning Analytics (SLA) can be usefully thought of as a subset of learning analytics approaches. SLA focuses on how learners build knowledge together in their cultural and social settings. In the context of online social learning, it takes into account both formal and informal educational environments, including networks and communities. The paper introduces the broad rationale for SLA by reviewing some of the key drivers that make social learning so important today. Five forms of SLA are identified, including those which are inherently social, and others which have social dimensions. The paper goes on to describe early work towards implementing these analytics on SocialLearn, an online learning space in use at the UK's Open University, and the challenges that this is raising. This work takes an iterative approach to analytics, encouraging learners to respond to and help to shape not only the analytics but also their associated recommendations.
社会学习分析:五种方法
本文提出,社会学习分析(SLA)可以被看作是学习分析方法的一个子集。二语习得关注学习者如何在他们的文化和社会环境中共同建立知识。在在线社会学习的背景下,它考虑了正式和非正式的教育环境,包括网络和社区。本文通过回顾当今社会学习如此重要的一些关键驱动因素,介绍了SLA的基本原理。本文确定了五种形式的SLA,包括那些具有内在社会性的形式,以及其他具有社会维度的形式。论文接着描述了在SocialLearn(英国开放大学正在使用的在线学习空间)上实施这些分析的早期工作,以及由此带来的挑战。这项工作采用了一种迭代的分析方法,鼓励学习者不仅响应并帮助塑造分析,而且还提供相关的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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