Appropriate interventions following the application of learning analytics

July 25, 2015 Tony Bates

Humble Pie 2

SAIDE (2015) Siyaphumelela Inaugural Conference May 14th – 15th 2015 SAIDE Newsletter, Vol. 21, No.3

Reading sources in the right order can avoid you having to eat humble pie. Immediately after posting Privacy and the Use of Learning Analytics in which I questioned the ability of learning analytics to suggest appropriate interventions, I came across this article in the South African Institute of Distance Education’s (SAIDE) newsletter about a conference in South Africa on Exploring the potential of data analytics to inform improved practice in higher education: connecting data and people.

At this conference, Professor Tim Renick, Vice-President of Georgia State University in the USA, reported on his institution’s accomplishment of eliminating race and income as a predictor of student success.

This has been achieved through implementing various initiatives based on data mining of twelve years’ worth of student data. The university’s early warning system, based on predictive analysis, has spawned a number of tested and refined low cost, scalable, innovative programmes such as:

  • supplemental instruction by former successful students;
  • formation of freshman learning communities which entail groups of 25 students enrolled in “meta-majors” ;
  • block scheduling of courses ;
  • re-tooled pedagogies involving adaptive learning software;
  • and small, prudent financial retention grants.

The combination of the above has resulted in phenomenally reduced student attrition.

I have no further comment (for once!). I would though be interested in yours.

Incidentally, there were other interesting articles in the SAIDE newsletter, including:

Each of these reports has important lessons for those interested in these issues that go far beyond the individual cases themselves. Well worth reading.

 

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