文章基本信息

标题：Big data for social media learning analytics: potentials and challenges
其他标题：Big data for social media learning analytics: potentials and challenges
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作者：Stefania Manca ; Luca Caviglione ; Juliana Elisa Raffaghelli 等
期刊名称：Je-LKS
印刷版ISSN：1826-6223
电子版ISSN：1971-8829
出版年度：2016
卷号：12
期号：2
页码：27-39
DOI：10.20368/1971-8829/1139
出版社：Casalini Libri
摘要：Today, the information gathered from massive learning platforms and social media sites allow deriving a very comprehensive set of learning information. To this aim, data mining techniques can surely help to gain proper insights, personalize learning experiences, formative assessments, performance measurements, as well as to develop new learning and instructional design models. Therefore, a core requirement is to classify, mix, filter and process the involved big data sources by means of proper learning and social learning analytics tools. In this perspective, the paper investigates the most promising applications and issues of big data for the design of the next-generation of massive learning platforms and social media sites. Specifically, it addresses the methodological tools and instruments for social learning analytics, pitfalls arising from the usage of open datasets, and privacy and security aspects. The paper also provides future research directions.
关键词：MOOCs; social media; social learning analytics; open datasets; big data; privacy & security; ethics