Key Note George Siemens Envisioning a research discipline AND a domain of practice
I’m referencing Doug Clowe’s blog as he provides an hugely comprehensive account of this superb key note. He’s even slipped a few nice graphics in. There’s lots of excellent material in here from George that I suspect will be surfaced in the summit day tomorrow.
Social Learning Analytics (SLA) The Open University Social Learning Analytics; Five approaches
Social Learning Analytics focus is on how learners build collaborations to support learning
How can we support the role of social networks in filtering and recommending resources for learning?
Will this help us prepare students for the skills they may need in years to come, those we cannot forecast?
Network analytics – the networks that can support learners to achieve
Discourse analytics – the ways in which learners engage in dialogue; includes contribution to challenge, reasoning, extension and evaluation – the 4 components identified as learning through discourse
Content Analytics – automated methods to examine, index and filter online media assets for learners (recommendation services)
Context Analytics – Analytic tools that expose, make use or seek to understand learners contexts (could be geographical location advising on nearby resources of interest)
Scenario – sat on an airplane with 15 minutes – query social learn for resources to learn about Vancouver. Social Learn offers appropriate content for subject and context
The authors provide a full paper
Along with their slides
In a very brief conversation with @sheilmcn we envisioned the national sharing of open content allowing students the opportunity to access as suits them (dashboards, widgits, whatever) with content choices referred to them via those 5 social analytics aspects. Now that would be cool.
Building a data governance model for learning analytics
Doug Clowe, Paul Hobson, Sabine Graf, Lori Lockyer
Quite a spread as one might imagine. Paul is interested in ethics, data security and enterprise architecture. Sabine in personalized referrals to students.
Paul: Free flowing data within Universities is far from being in place. Data governance is OK for transactional data, identity and access management but analytics is very much in early days. Privacy issues need consideration. Students owning their own data. Legislation is prohibitive. Being precise about what people are going to do with the data afterwards – on the research side, ethics I the cover, on the administrative side it’s more legislative.
The possibilities for analytics seem endless – what could be done is very different to what should be done.
Doug: mains strategy about data governance has been not to think about it
The paradox of freedom from surveillance vs freedom to gain access to data
Surprise is key – if an individual is surprised then we have screwed up
The old ‘collect just enough data then dispose of it’ no longer holds in analytics
Instinctually open is good, but learning should be a safe space
Anonymising data is ‘hard’
We might aspire to biomedical research approaches as belt and braces.
The boundary between research and practice shouldn’t be blurred RE ethics and Policy
Involving the stakeholders in the design process of data governance is key
Analytics for the institution vs analytics for the individuals
Ownership of data vs ownership of inferences. We should look to the DPA for a minimum standard
Don’t collect data that could cause harm
When a student enters University, they enter a system, contractually a partnership / ownership of student ‘traces’ is important to maintain.
That’s all for LAK12. Next up the SOLAR AGM. LAK13 will be in Leuven, Belgium 8 – 12 April 2013