Week 9 lifestream review

This week gave me chance to delve into ideas around ‘Big Data’, ‘Data-Mining’ and learning analytics.  Hearing the term ‘Big Data’ at various meetings over the last year, I realised that the focus was always on the data itself, rather than the methods used to ‘mine’ it.  We know that detailed analytics gives us further understanding of a learner’s progress, but it is only relevant if we are ‘reactive’ to the data.  Allowing algorithms to construct this support and provide suggestions is becoming more popular especially within HE establishments who believe the more learner data the better, but can we rely on these analytics?  The big question which came out this week was, ‘Does the comprehensive use of analytics, statistics and algorithms improve the educational journey, and more importantly does it make learners more successful?’.

As a starting point, I found a useful article around ‘What can universities learn from big data?’ which clarified the opportunities available to HE establishments if they make effective use of this data.  Increased student retention and teacher effectiveness through ‘fine tuning’ were particular highlights within the article.  Making the change from traditional ‘retrospective’ process, to a free flowing proactive approach can certainly help provide an up to the minute report of each learner and tutor, but is this overly cautious and even necessary and is there a need to constantly put students and staff under pressure to maintain or increase their performance through analytics?  There may be some ethical issues around this subject.

I enjoyed the Tweetorial earlier in the week as well as hearing different approaches to the subject from my ‘classmates’ and tutors.  A lot of the tweets were in regard to positive ideologies surrounding ‘Big data’ and the inevitable benefit on education from ‘cradle to career’ (@NicholasJenkin8 didn’t like this term too much!).


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