Reporting back from the SURF Academy on the Grand Challenges to Learning Analytics.

Thursday May 28th in Utrecht SURF a work shop was held focussing on the grand challenges to Learning Analytics. This was done in conjunction with the release of a report setting the boundaries by SURF. As co-chair of the Special Interest Group on Learning Analytics and also as one of the author’s of the report I had the distinct pleasure of presenting on the subject of what is needed at Universities in terms of Infrastructure to support learning Analytics.

Here are the links to the presentation and the report:

Learning Analytics is an up and coming field of interest in the Higher Educational sector. Spearheaded by student retention systems and nicely visual dashboards there is currently a cycle heavy investment by numerous Universities. Examples of well respected retention systems include Course Signals and the Open Academic Analytics Initiative (http://www.itap.purdue.edu/studio/signals/ , http://nextgenlearning.org/grantee/marist-college).

Learning Analytics is also a relatively new field where the definitions are settling down. It is quite possible to have different people in the same room, discussing the same subject, but actually talking about different things. Therefore, it is important to agree a common set of definitions. One widely applied definition for Learning Analytics (http://www.learninganalytics.net/?p=131) is “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs”. Common LA services include prediction of student failure and interventions, dashboards for group mirroring or helping teachers focus their attention on students, and mobile apps to support students learning trajectories.

Data centralism is one of the core needs for efficient Learning Analytics services. The understanding of what data we have, the breaking down of data silos and the process of cleaning the data is costly in time, money and politics. One can argue that there is an emerging digital divide for researchers in the Learning Analytics field and later students for Universities with data centralism and those without. We have seen this before within the commercial domain. Big data methodology have already had impact on the competitive value between businesses and is now the methodologies are evolving and working their way into Education.

The question I challenged the audience to answer by the end of the presentation at SURF was:

There will be a digital divide between Higher ED organisations with data centralism and those without. Those organisations with data centralism will have a significant competitive advantage related to Learning analytics services.

The audiences conclusion was that he Higher Educational market will speak and the answer over the coming few years will become obvious.

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