Am trying to slowly get back into the learning analytics literature as part of writing a paper. The following is an ad hoc collection of comments/reflections on a few learning analytics papers.
Definitions, processes and potentials
Social learning analytics
Social Learning Analytics (Buckingham Shum and Ferguson, 2011) is something I’ve been meaning to read for awhile.
Three challenges and opportunities for the design of social learning analytics
- challenge of implementing analytics with pedagogical and ethical integrity given questions of power and control over data
Draws on existing disciplinary and ethical critiques of “new forms of measurement and classification” e.g. Bowker and Staur (1999)…e.g. if it ain’t measured, it doesn’t exist.
Expands this to suggest that much of analytics focuses on data generated as a by-product of online learning, not as an intentional form of evidence of learning. Gives 5 “variants on longstanding debates” that apply to analytics.
- the challenge given by an increasingly turbulent educational landscape
Identifies 5 phenomena that create a new context for learning and consequently suggest the need for a rethink of analytics. They are
- Technological drivers
- shift to “free” and “open”.
- cultural shifts in social values
- innovation requires social learning
- challenges to educational institutions
Some of this I might argue against. But the section on “innovation requires social learning” is much more interesting.
Uses “the power of pull” to argue the point. This includes the idea that much of the knowledge in the new context is tacit. Which means it can’t be extracted and written down. i.e. analytics can’t measure it.
Questions/thought: Raises the idea of analytics designed to help the construction/sharing of tacit/shared knowledge.
- understand different types of social learning analytic. ,/li>
The core proposition is that with the unprecedented amounts of digital data now becoming available about learners’ activities and interests, from educational institutions and elsewhere online, there is significant potential to make better use of this data to improve learning outcomes.
I like this quote because it suggests to me assumptions that can be challenged. e.g. while there may be a lot of data generated by LMS (quality), the overal quantity of the data or the insight about learning that can be drawn from that data is questionable.
A major part of the paper spends time outlining the challenges and opportunities.
Then the initial taxonomy of five types of social learning analytic is introduced.
- Social learning network analysis
- Social learning discourse analysis
- Social learning content analysis
- Social learning dispositions analysis
- Social learning context analysis
Finally, potential futures of learning analytics, an interesting list is provided.
Another overview of the origins of learning analytics.
The evidence is that a growing number of universities are
implementing data warehouse infrastructures in readiness for a future in which they see analytics as a key strategic asset (Stiles et al 2011)
Question: What follows is a brief description of a project at OU that illustrates this organisational trend. It would be interesting to do research that looked at these institutions and found out how they are going, how they are implemented, their impacts and more importantly, how they are being worked around by members of the institution.