(W10) What is Social Learning Analytics?

     Learning Analytics has become a popular domain to collect, analyze and interpret behavior and performance of learners. Specifically for networked knowledge activities, Social Learning Analytics (SLA) has emerged as a subset of the major domain. Let's explore what SLA means.

    Buckingham Shum and Ferguson (2012) defined SLA as the collection and measurement of students’ produced digital artefacts and online interactions in formal and informal settings in order to analyze their activities, social behaviors, and knowledge creation in a social learning setting. SLA is concerned with the social learning environment where learning takes place.

    There are five categories of SLA under the umbrella of “inherent social analytics” and “socialized analytics.” The inherent SLA categories include:

  • (i)

    social learning network analytics (SLNA), which employ networked approaches to study student interactions when they are socially engaged; and

  • (ii)

    social learning discourse analytics (SLDA), focused on analyzing textually based constructed knowledge through large amounts of text generated during online interactions. The socialized SLA categories include:

  • (iii)

    social learning content analytics, which uses automated methods to examine, index, and filter learner generated content (e.g. documents, images, logos);

  • (iv)

    social learning context analytics, which involves analytic tools that expose, make use of, or seek to understand learning contexts; and

  • (v)

    social learning disposition analytics, which combines learning dispositions data with data extracted from computer assisted, formative assessments


    The conceptualization of SLA helps me understand objects of analyzing in networked knowledge activities and their potential implications. By deciphering behaviors of learners or learner groups in their own online knowledge activities and interactions with others, we can understand their motivation, rationale and disposition of taking specific actions in a social-cultural learning context. The insights help us make qualitative assessments of learners in addition to their performance numeric evaluations, and generate relevant interventions to fulfill learning objectives.


    Reference: 

    Simon Buckingham Shum, & Rebecca Ferguson. (2012). Social Learning Analytics. Journal of Educational Technology & Society, 15(3), 3–26. http://www.jstor.org/stable/jeductechsoci.15.3.3

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