Associated with the dominance of the LMS in higher education is a burgeoning interest in educational data mining which is the harvesting and analysis of user activity and interaction data captured by the LMS (Macfadyen & Dawson, 2010). George Siemens (2011) blogged recently about the three distinct terms used when talking about the application of educational data mining.
- Educational data mining is concerned with developing methods for exploring the unique types of data that come from educational settings.
- Learning analytics is the measurement, collection, analysis and reporting of data about learners and their context.
- Academic analytics a mixture. More aligned with traditional business intelligence in higher education.
From my perspective there could be another dimension to these definitions and that is the difference between tactical analytics and strategic analytics. Tactical analytics is a subset of learning analytics designed to assist the student or teacher at their point of need and at the time of need. It is about what is happening within this context right now. Academic analytics is more strategic analytics in that it is (perhaps aggregated) data that is analysed retrospectively. Actually I think it may have been George who alluded to something like this in one of his Slideshare presentations.
On another note, here at CQUniversity we have been tinkering with learning analytics for some time now. The trouble I am beginning to appreciate is that analytics is a retrospective indicator of what has happened in a complex adaptive system and consequently only provides limited insight into the here and now. As the interdependent systems and their agents that constitute an e-learning environment evolve and adapt, the measurements of their behaviors and interactions within the e-learning environment will also change and inhibit their predictive value. Based on this and in my particular context, I am beginning to think that the bigger picture is how educational data mining, learning analytics and academic analytics contribute to creating interventions in e-learning when e-learning is considered as a complex system. Especially when considered against a backdrop of universities being managed like a ‘machine’ with replaceable parts and a belief that problems can be solved by rational and reducible deduction (Plsek & Greenhalgh, 2001).
Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an ‚Äúearly warning system‚Äù for educators: A proof of concept. [Article]. Computers & Education, 54(2), 588-599.
Plsek, P. E., & Greenhalgh, T. (2001). Complexity science: The challenge of complexity in health care. BMJ (Clinical Research Ed.), 323(7313), 625-628.
Siemens, G. (2011). Learning and Knowledge Analytics. Retrieved 1/11/2011, 2011, from http://www.learninganalytics.net/?p=131