Originally posted on Erik Duval's Weblog:
In my view, Learning Analytics is about collecting traces that learners leave behind and using those traces to improve learning. Educational Data Minging can process the traces algorithmically and point out patterns or compute indicators. My personal interest is more in using the traces in order to empower learners to be ‘better learners’.
My team focuses on building dashboards that visualize the traces in ways that help learners or teachers to steer the learning process. I like this approach because it focuses on helping people rather than on automating the process. It is inspired by a ‘modest computing’ approach where the technology is used to support what we want people to be good at (being aware of what is going on, making decisions, …) by leveraging what computers are good at (repetitive, boring tasks…).
Of course, capturing meaningful learning traces is something that both we and the EDM community struggle with. Translating those traces into visual representations and feedback that support learning is another challenge: the danger of presenting meaningless eye candy or networks that confuse rather than help is all too real.
Both our work and that of the EDM community is also difficult to evaluate: we can (and do!) evaluate usability and usefulness, but assessing real learning impact is hard – both on a practical, logistical level (as it requires longitudinal studies) as well as on a more methodological level (as impact is ‘messy’ and it is difficult to isolate the effect of the intervention that we want to evaluate).
In both these areas, we may be able to make better progress by exchanging our experiences. There is also an opportunity to combine both approaches: for instance, we can use visualization techniques to help people understand what data mining algorithms come up with and why. In that way, work on visualization can help to increase understanding of and trust in what the EDM community achieves.
Does this sound right to you? Do you have another view on how (educational) data mining and (learning) analytics relate to one another?