Towards Analytics Literacy

 

In his post, Analytics Literacy is a Major Limiter of Ed Tech Growth, Michael Feldstein argues that there is a lack of basic literacy in the Ed Tech and Learning and Development communities.  He points out that analytics is as much about storytelling and sensemaking as it is about data.  We intuitively create stories about the data we see, it is the core of hypothesis creation.  Through repetitive and progressive testing of hypotheses, we come to trust the story that algorithms tell.  This builds analytic literacy.

Feldstein provides several excellent examples of the role of analytic literacy in evaluating student performance based upon logins to software,  weather forecasts of precipitation, and the recent US Presidential election.  When we lack the literacy to get the story right, we start to distrust the data or the analytics, not our literacy.

If we, as a culture, lack the basic literacy to have clear intuitions about what “a 70% chance” means, then how likely is it that we won’t have shocks that cause us to distrust our learning analytics because we didn’t understand their assumptions and limitations?

He uses the medical community’s move to scientific methodologies a century ago as an example of the transformation that the learning community now needs to undertake regarding analytics and performance.  But he also points out that we need to avoid placing all our analytical trust in various technology tools.

Using a personal story about a medical issue, he discusses how dependence upon various diagnostic tools didn’t reveal the cause of back pain he was experiencing.  It was a doctor touching his back and feeling the benign fatty tumor that was present before he was correctly diagnosed.

He finishes by concluding that the “training of learning and development professionals needs to make a radical change to  transform our teaching culture into one of learning science and data science literacy.”  While not losing the intuition and observation skills that have propelled our field to date.

I’m not sure that I agree with Feldstein that incorporating learning analytics into our profession is going to take a radical transformation.  15-20 years ago, in the early days of elearning, there were predictions that the advent of online, digital tools would decimate the L&D profession.  Which, supposedly was so rooted in brick and mortar, face-to-face training delivery that it wouldn’t be able to adapt.

Well, time has shown the world what we already knew about ourselves, we will adapt in whatever way we need to achieve our goal of helping people learn.  In the case of elearning, not only did we adapt, we thrived.  We turned the change to our advantage to improve learning across the board.

Analytics, statistics, and Big Data are a bit of a foreign language for most L&D pros, but it fits well with our trained skills of needs analysis and evaluation.  We cherish any information that will enable us to design better learning experiences.  There will be an additional benefit in that we will be able to demonstrate our link to not only the success of the businesses we work within, but we will resonate more closely with the management culture of our organizations.   Our evidence of success will look like their evidence of success.  Learning analytics has the promise of finally putting us in a position of being peers among peers in our organizations.  That may well be the best carrot for L&D professionals in taking on this new challenge.

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