#lak11 getting #learning #analytics on track demands interdisciplinary teams and connectedness

Grasping all the factors of learning analytics is at the core of LAK11 course. In the first week John Fritz took the mic of the Elluminate session (you can see the recording here) and got all the LAK11 participants introduced to learning analythics.

In order for learning analytics to take off, strategies must be in place that clearly recommend teacher or trainer interventions to pick-up those learners who are at risk of dropping out of courses or trainings.

Need for interdisciplinary teams
While listening to his presentation, I immediately got the idea that the mere fact of learning analytics is interdisciplinary. You cannot analyze learner metrics and hope to take educational action, if the educational intervention team is not connecting different fields together.

John mentioned in this presentation that the biggest question for learning analytics is: how do we move on, where do we go from here? John mentioned that most educational and training institutes are still at the analysis stage, but they do not move on to an intervention stage to get their learners on track. I think this is part of the fact that within institutions interdisciplinary teams are scarce. Learning analytics demands more than the data, it demands true understanding of learning theories, of sociology, of instructional design, of psychology, IT (if not more). So if an institute wants to cross the barrier of mere analyzing their students, they will have to build a framework, or at least see if framework builders are willing to work together in their institutes.

Is there such a thing as zone of proximal development for researcher teams?
I feel that if a new research that expands across many fields of interest has any chance of succeeding, it must connect experts from all of these fields. That is easier said then done, as the diversity of fields might put the experts so far away, that there is no common ground on which to stand and understand themselves. So, getting together a mix of disciplines is not enough. In a way all of these experts must be within their own zones of proximal development, their knowledge must be close enough to be able to link to the newly to construct knowledge in an understandable way.
If an interdisciplinary task-force to work on learning analytics and its strategies needs to be put together, I think the profiles of this team must be overlapping just enough to allow cross-understanding.

Self-regulated learning
John also talked about giving the learners the tools to adjust their learning. This is a huge meta-learning challenge, for it demands that learners understand many of the factors that influence their learning, which seems difficult to me. Nevertheless, what John says about this is interesting. It might help some students, but at the same time I feel that if even the researchers do not come up with strong solutions, how can we put most of the burden on the learners? (but I do agree with John that learners must also take responsibility for their learning). John mentions a great resource for research on self-regulated learning: Zimmerman.