Learning and Knowledge Analytics

The Horizon 2011 report predicted Learning Analytics to be adopted over the next five years. The field of Learning Analytics itself is hardly new. It has its origins in traditional data collection and statistical methods. The reason why it attracts so much attention now is that we live in a world where data exists in abundance and data collection has become cheap. Furthermore, the data economy is still massively growing since the volume of data created by new technologies and people doubles in shorter and shorter time intervals.

My interest in Learning and Knowledge Analytics is mainly directed towards two perspectives. Firstly, there is benefits to be had from harvesting learner data better, looking at the messages they contain, and presenting this information back to the learner (or teacher) to allow them to reflect upon their activities or to compare themselves with peers or ideals. This can reveal insights that were largely invisible before.

Secondly, the field of educational data mining and learning analytics has substantial ethical implications that need to be investigated. First and foremost is the question of who owns the data about a person's (online) behaviour. Then there is the issue of the analytics design and what it actually reflects. And, finally, there is the competence issue of what we can expect users to do when they are confronted with the analytics' results.

As the field develops, a diversity of challenges and research questions arise. In addition to programming and hardcore algorithmic debates around data models, metrics, weightings and indicators, there are questions about the pedagogic application of learning analytics, and the contribution to theory-based learning sciences. What does data tell us about the theories that we hold about learning. Can learning analytics help us understand better the learning processes and validity of theoretical beliefs? Can LA give us insights into hitherto hidden patterns and dependencies?

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