This presentation 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
Providing the context for our work on social learning analytics OU has a quarter of a million students – over 16,000 of those are outside the UK More than 50 million iTunes downloads Developing SocialLearn - a social media space tuned for learning.
When it comes to analytics, the OU is well placed to work towards these aims of learning analytics We have a track record of research We have a lot of data to work with well over a million students in that 40 years, well over a million informal learners. Lots of researchers at our university are interested in how we can used this data to support learners and teachers.
A new challenge We need to go beyond the institutional figures about retention, success rates and students at risk We need to adapt to an educational landscape where content expertise is no longer key; where we have to develop students who know how to learn, and are able to keep on learning, finding their own support and resources, even when things get tough Why this pic? – screenshots from the ‘Shift Happens’ video on YouTube
So we are interested in how people learn together. Not just in courses and cohorts, we are also interested in networks, communities and affinity groups. Two perspectives: 1. how can the individual learn more effectively in social contexts? 2. how can these groups function more effectively to support learning?
Some of the reasons we ned social learning analytics: Social media are now ubiquitous, and support a lot of learning interactions Most of this is off the radar for formal education, there’s just too much of it, and learners often want to keep their social interaction separate from their formal education. Social learning analytics could provide tools to support learning in these situations Why this pic? – Google Plus, Twitter and Facebook being used for learning
Learners are faced with a deluge of information Just one Twitter hashtag can provide ideas, information, resources and support Social learning analytics could help to filter and recommend resources Could help to develop effective learning networks Why this pic? – the #phdchat community helping with filtering and recommendations
Knowledge, rather than land, labour or capital is now the key wealth-generating resource Constant change in society is now the norm We don’t know which knowledge and skills will be useful in the future, so we need ‘knowledge-age skills’ These included group-focused skills such as collaboration, communication and social responsibility Why this pic? - Two of the many knowledge-age skills frameworks
World Values Survey 1997: covered 43 societies, representing 70% of the world’s population. Shoed a shift away from hierarchy, authority, conformity Towards autonomy and diversity In “postmodernity”, as Inglehart used the term, people value autonomy and diversity over authority, hierarchy, and conformity. According to Inglehart, ‘postmodern values bring declining confidence in religious, political, and even scientific authority; they also bring a growing mass desire for participation and self-expression.’ We find these results interesting: on the one hand it is easy to recognise this shift in wealthy nations, but this shift seems also to be reflected even in the less developed regions surveyed, where poverty is still clearly a daily reality.
Sociocultural understanding of learning makes it clear that knowledge isn’t simply transferred to us There is an active process of knowledge construction The quality of the interaction round resources is important when making sense of the learning resources Why this pic? – Dragan in a learning analytics MOOC, the learning comes from the chat panel on the left, as well as from the presenter and his slides
Sociocultural understanding of learning makes it clear that learning is a social process It takes place using tools SLA could support learners to engage with these group tools, and in these different group environments Why this pic? – screenshot from ALT-C, using Internet, livestream, twitter stream while at F2F conference
We are drawing on a set of established research and tools. These are the ones we are currently exploring Network analytics – interpersonal relationships Discourse analytics – primary tool for knowledge construction Content analytics – Resources have a social dimension – they are created, tagged, rated, reviewed, bookmarked Disposition analytics – these include learning relationships, and are developed through mentored conversations Context analytics – giving access to the people, groups, social settings
Social network analytics are well represented at this conference Several tools being developed to support them, including SNAPP and Network Awareness Tool Already in use, supporting both learners and teachers. We are interested not only in feeding back to individuals, but also to groups – what is working well, what do successful groups look like? For each set of learning analytics – there is a slide on description or possible uses, and one on how we are implementing it.
Intern currently working on social network analytics on SocialLearn Moving away from interview-based input to automatic input. We are interested in helping groups to find each other A way of filtering the resources on the platform – if there are thousands of people how do you find the ones to support your learning? Highlighting relationships between individuals and groupings that are not obvious otherwise Why this pic? – Screenshot from Maarten de Laat’s Network Awareness Tool
We have seen a lot of social network analytics at this conference, but less emphasis on discourse or semantic analysis From a sociocultural perspective, language and dialogue are crucial tools in the development of knowledge The work of Neil Mercer and his colleagues has shown that effective dialogue can be taught, and can significantly improve results
Intern is currently working on data from Elluminate chat to investigate whether it is possible to generate this sort of analytic This focuses on four elements of exploratory dialogue, and it provides automated feedback This isn’t black-box feedback, though, it provides information for reflection and also ideas for change Why this pic? – discourse analytics mock-up from journal paper
This is a socialised analytic – recommendations are not necessarily social (note that this is not content analysis – which is a well-established method of analysis) What we are interested in here is whether we can make use of social information – ratings, recommendations, comments, bookmarks, to help learners to navigate information Recommendations aren’t necessarily based on interest – we could look for resources that challenge our views, that provide alternative perspectives
Initial work by Suzanne Little allows the SocialLearn toolbar to identify the images on a page, and then to compare those against a database Visual Similarity Search allows you to recommend other places where the image is used The socialised element could come when combined with a site like iSpot The analytic provides a recommendation – but it is users who consider and rate that recommendation And, in the case of iSpot, that potentially takes us into another area – how do we create analytics that assess whose judgment is likely to be reliable?
This takes into a personal domain – the characteristics of a good learner Validated over a decade – but also a list reflected in the Twitter stream on Monday Increase people’s capacity to learn, and their capacity to learn in a variety of situations Why this pic? - to flag up the title of Monday’s talk – available on Simon Buckingham Shum’s Slideshare
The ELLI spider is already available as a form of learning analytic (If you’re interested in finding out more, or trialing this, contact Ruth Deakin Crick) The social aspect appears in several ways. First, as the basis for a mentored conversation (see yesterday’s paper on mentoring analytics) Second, can we derive a useful profile from interactions on a social learning platform? This is what Shaofu is currently working on Why this pic? – ELLI pics from journal paper
Two approaches here. One takes your context as static – this might be your context when you are enrolled in a class, or working as part of a group The other takes a more dynamic approach and seeks to shift the possibilities available to you in your context – for example, you are walking down the street, and the analytics make you aware of a nearby resource or fellow learner (This view emerged from the MOBILEARN project)
This work is really waiting for development on the SocialLearn main site, before rolling it out as an app This is a mock-up from our app developers, showing how this could work The socialised aspect here comes when these recommendations are tagged, grouped and rated – adding richness to the process Why this pic? – mobile app mock-up from journal paper
Moving forward, presenting these analytics to learners, teachers and groups in a useful and comprehensible way Why this pic? – Dashboard mock-up from journal paper
Social learning analytics: LAK 2012
Social Learning Analytics:Five ApproachesRebecca FergusonSimon Buckingham ShumThe Open University, UK
Learning analyticsDeveloping new tools for learners and teachersdrawing on experience from the learning sciencesintention of understanding and optimizingnot only learningbut also the environments in which it takes place
Social learning analyticsSocial learning analytics focus on how learnersbuild knowledge together in their cultural andsocial settings.In the context of online social learning, theseanalytics take into account both formal andinformal educational environments, includingnetworks and communities.
Why social learning analytics?Social media Support learning-related reflection on interpersonal relationships and interactions
Why social learning analytics?Free and open content New Usefulideas resources Key hashtagsHelpful Support info networks Support the role of social networks in filtering and recommending resources
Why social learning analytics?Living in the knowledge age Support learners to assess their progress in terms of knowledge-age skills
Why social learning analytics?Growth in ‘postmaterialist’ values www.worldvaluessurvey.org World Values Survey 1990-93 Post-materialist values by birth cohort in Western democracies, Eastern Europe, East Asia and Africa. (Inglehart, 1997) The shift to participatory technology is powered by a shift in cultural values Source: R Inglehart, 1997
Why social learning analytics?Sociocultural understandings Increase learner proficiency in the use of educational dialogue
Why social learning analytics?Sociocultural understandings Enable learners to engage proficiently with a range of tools and social settings
Social analytics: potential usesNetwork analyticsIdentify individuals who support my learningIdentify individuals with relevant interestsIdentify origins of conflictsIdentify groupings that could support learningProvide feedback to groups and group leaders
Social analytics: potential usesDiscourse analyticsThe ways in which learners engage in dialogueindicate how they engage with the ideas of others,how they relate those ideas to their understandingand how they explain their own point of view. • Disputational dialogue • Cumulative dialogue • Exploratory dialogue
Socialized analytics: potential usesContent analyticsVarious automated methods used to examine, indexand filter online media assets for learners.These analytics may be used to providerecommendations of resources tailored to the needsof an individual or a group of learners.
Socialized analytics: potential usesDisposition analyticsDispositions can be used to render visible thecomplex mixture of experience, motivation andintelligences that make up an individual’s capacityfor lifelong learning and influence responses tolearning opportunities
Socialized analytics: potential usesContext analyticsAnalytic tools that expose, make use of or seek tounderstand learners’ contexts. These analytics maybe used alone, or may be employed as higher-leveltools, pulling together data produced by otheranalytics. Context as a dynamic process – a mobile device can present content, options and resources that support learning activities in this location at this time.
Take-home message… Online social learning is redefining the learning landscapeSo learning analytics must enhance the process – by building on theories of effective learning We have shown you five classes of SLA SocialLearn provides us with an innovation platform to test these ideas