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.

#WOLWeek Day 2 – Make a Connection

A day late in posting, but…..

Day 2: Make a Connection

Ask yourself who else might be interested in your work to fulfil that purpose.  Find someone new with whom you don’t usually interact. Your challenge for day 2 is to introduce yourself to this person or otherwise make a connection.  Join a group that they are a part of, follow them on social media, ask a friend to make an introduction.  Whatever you do take one step towards enriching the networks of people around your work. Feel free to repeat this step to connect a community around your work. A great step at this stage can be to start a Working Out Loud Circle with some new connections.  Working Out Loud circles are a great way to deepen relationships in an ongoing way.

What I am Doing

I’m following the spirt of today’s action, if not the letter.  I’ve made the decision to attend the xAPI Camp next Tuesday in Las Vegas where I can make multiple connections and expand my network.

#WOLWeek Day 1 – Share a Purpose

Michelle Ocker’s Day 1 post alerted me to the fact that Working Out Loud Week (#WOLWeek) is 7-13 November 2016. I’m using it as an opportunity to promote Working Out Loud (WOL) and give my own practices a boost by following the 7 days worth of actions to get you started working out loud.

Working Out Loud is one one of the practices being promoted to make social and informal learning more effective.

The seven actions for the week:


Day 1: Share a Purpose – Instructions

Choose some purpose that is important to you to make the focus of your #wolweek efforts. This purpose may be delivering a great outcome in a project for a group of stakeholders or it could be a personal ambition in your life or your career.  The purpose doesn’t have to be something big but it needs to be something that is worthwhile for you and others to pursue.  When you have chosen the purpose, share that you are working on it with some relevant stakeholders.

My goal for this week is:

Layout a plan of action that will guide me in building a non-technical expertise in the new xAPI standard for interoperability of learning data.

Tne xAPI standard in being developed by The Advanced Distributed (ADL) Learning and rolled out by DISC (Data Interoperability Standards Consortium).  xAPI is short for Experience API.  It is also known to many by its original project name Tin Can API  It will “replace” SCORM  over time. Continue reading “#WOLWeek Day 1 – Share a Purpose”