Big data for social media learning analytics: Potentials and challenges. Manca, S., Caviglione, L., & Raffaghelli, J., E. 2016. abstract bibtex Today, the information gathered from massive learning platforms and social media sites allows to derive a very comprehensive set of learning information. To this aim, data mining techniques can surely help to gain proper insights, personalise 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 big data sources involved by means of proper learning and social learning analytics tools. In this perspective, this 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.
@misc{
title = {Big data for social media learning analytics: Potentials and challenges},
type = {misc},
year = {2016},
source = {Journal of E-Learning and Knowledge Society},
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keywords = {Big data,Data anonymization,Ethics,MOOCs,Open datasets,Privacy & security,Social learning analytics,Social media},
pages = {27-39},
volume = {12},
issue = {2},
publisher = {Italian e-Learning Association},
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abstract = {Today, the information gathered from massive learning platforms and social media sites allows to derive a very comprehensive set of learning information. To this aim, data mining techniques can surely help to gain proper insights, personalise 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 big data sources involved by means of proper learning and social learning analytics tools. In this perspective, this 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.},
bibtype = {misc},
author = {Manca, Stefania and Caviglione, Luca and Raffaghelli, Juliana Elisa}
}
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