Bodong Chen

Crisscross Landscapes

Notes: Ferguson - 2012 - Social learning analytics



Citekey: @Ferguson2012

Ferguson, R., & Shum, S. B. (2012). Social learning analytics. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12 (p. 23). New York, USA: ACM Press. doi:10.11452330601.2330616


People commonly misinterpret social learning analytics as social network analysis. This article addresses this misinterpretation.


the perspective from that of the institution gathering data about learners in order to inform organisational objectives, to that of providing new tools for the learner and teacher, drawing on experience from the learning sciences with the intention of understanding and optimizing not only learning but also the environments in which it takes place. (p. 1)

we propose that Social Learning Analytics (SLA) can be usefully thought of as a subset of learning analytics, which draws on the substantial body of work evidencing that new skills and ideas are not solely individual achievements, but are developed, carried forward, and passed on through interaction and collaboration. A socio-cultural strand of educational research demonstrates how language is itself one of the primary tools through which learners construct meaning, and its use is influenced by the aims, feelings and relationships of their users, all of which shift according to context 3. Another strand of research emphasises that learning cannot be understood by focusing solely on the cognition, development or behaviour of individual learners; neither can it be understood without reference to its situated nature [4, 5]. (p. 1)

this paper cites numerous examples of related work in context. Instead, it groups a range of pre-existing and new tools and approaches to form the basis of a coherent set. (p. 1)

Learning analytics shift (p. 1)


2.4 Innovationdependsonsocialconnection (p. 3)

In a succinct synthesis of the literature, Hagel, et al. [13] argue that social learning is the only way in which organisations can cope with the unprecedented turbulence they now face. They invoke the concept of ‘pull’ as an umbrella term to signal some fundamental shifts in the ways in which we catalyse learning and innovation, and argue that the world is changing so rapidly that useful knowledge/understanding (in contrast to data or information) is rarely well codified, indexed or formalized, while socially transmitted knowledge is growing in importance as a source of timely, trustworthy insight. This leads them to highlight quality of interpersonal relationships, tacit knowing, discourse and personal passion as key capacities to foster, as we move in business from mere transactional relationships, to building and sustaining more meaningful relationships. (p. 3)

3.1 Social learning network analytics (p. 3)

Social network analysis investigates ties, relations, roles and network formations, and a social learning network analysis is concerned with how these are developed and maintained to support learning [15]. (p. 3)

We now introduce five categories of analytic whose foci are driven by the implications of the drivers reviewed above. The first two categories are inherently social, while the other three can be ‘socialized’, ie. usefully applied in social settings: (p. 3)

• social network analytics — interpersonal relationships define social platforms • discourse analytics —language is a primary tool for knowledge negotiation and construction • content analytics — user-generated content is one of the defining characteristics of Web 2.0 • disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation • context analytics — mobile computing is transforming access to both people and content. (p. 3)

3.2 Social learning discourse analytics (p. 3)

Educational success and failure have been related to the quality of learners’ educational dialogue [24]. Social learning discourse analytics can be employed to analyse, and potentially to influence, dialogue quality. (p. 4)

Mercer and his colleagues distinguished three social modes of thinking used by groups of learners in face-to-face environments: disputational, cumulative and exploratory talk [25-28]. (p. 4)

Learning analytics researchers have built on this work to provide insight into textual discourse in online learning [29, 30], providing a bridge to the world of social learning analytics. (p. 4)

4.2 Social learning disposition analytics (p. 4)

Learners who are prepared to learn and are open to new ideas have the potential to make good use of these resources and tools. A well established research programme has identified, theoretically, empirically and statistically, a seven-dimensional model of learning dispositions, termed ‘learning power’ [38]. These dispositions can be used to render visible the complex mixture of experience, motivation and intelligences that make up an individual’s capacity for lifelong learning and influence responses to learning opportunities 39


4.1 Sociallearningcontentanalytics (p. 4)

‘Content analytics’ is used here as a broad heading for the various automated methods used to examine, index and filter online media assets for learners. (p. 4)

These analytics may be used to provide recommendations of resources tailored to the needs of an individual or a group of learners. This is a very fast-moving field, and the state of the art in textual and video information retrieval tools is displayed annually in competitions such as the Text Retrieval Conference [see 33 for a review]. (p. 4)

From a social learning perspective, three elements of disposition analytics are particularly important. Firstly, they draw learners’ attention to the importance of relationships and interdependence as one of the seven key learning dispositions. Secondly, they can be used to support learners as they reflect on their ways of (p. 4)

perceiving, processing and reacting to learning interactions. Finally, they play a central role in an extended mentoring relationship. This type of relationship has an important role in online social learning, especially when learning is informal and not teacher-led. (p. 5)

4.3 Sociallearningcontextanalytics (p. 5)

‘Context analytics’ are the analytic tools that expose, make use of or seek to understand these contexts. (p. 5)

5.1 Implementingsociallearningnetwork analytics (p. 5)

• identifying disconnected students • identifying key information brokers within a class • indicating the extent to which a learning community is developing within a class 50

Social learning network feedback for a group or moderator will seek to use what is known about effective group structure and dynamics and feed this back for reflection [51]. (p. 5)

5.2 Implementing social learning discourse analytics (p. 6)

Key characteristics of exploratory dialogue include challenge, evaluation, reasoning and extension. Initial research suggests that these are signaled in forum interaction by key words and phrases [29]. For example: ‘alternative’, ‘but if’ and ‘I don’t believe’ suggest challenge; ‘good point’, ‘important’ and ‘how much’ suggest evaluation; ‘next step’, ‘it’s like’ and ‘relates to’ suggest extension, and ‘does that mean’, ‘my understanding’ and ‘take your point’ suggest reasoning. Figure 1 shows how a visualization of these elements of dialogue could be presented to learners, together with recommendations. (p. 6)

  1. CONCLUSION Social learning analytics make use of data generated by learners’ online activity in order to identify behaviours and patterns within the learning environment that signify effective process. The intention is to make these visible to learners, to learning groups and to teachers, together with recommendations that spark and support learning. In order to do this, these analytics make use of data generated when learners are socially engaged. This engagement includes both direct interaction – particularly dialogue – and indirect interaction, when learners leave behind ratings, recommendations or other activity traces that can influence the actions of others. Another important source of data consists of users’ responses to these analytics and their associated visualizations and recommendations (p. 9)

[50] Bakharia, A., Heathcote, E. and Dawson, S., Social networks adapting pedagogical practice: SNAPP. In: Same Places, Different Spaces. ascilite 2009 (Auckland, 2009). (p. 11)

[51] Haythornthwaite, C., Learning relations and networks in web-based communities. International Journal of Web Based Communities, 4, 2, (2008), 140-158.