By Terry Anderson, professor in Distance Education, Athabasca University.

As an educational technologist, I seem always infatuated with the latest tools, even as I grow increasingly alert to what is lost as well as what is gained from their use.

Learning analytics joins the family of mostly commercial applications based on “big data”. These tools promises — perhaps too optimistically — to replace metaphors of information overload, info glut and obesity with the more optimistic sense that, although “big” we can effectively gather and interpret the torrent of digital information traces left by distance teachers and learners.

Both distance educators and students have traditionally suffered from a lack of awareness of each other. The black hole of geography and temporal distance has, in the past, only been penetrated by sporadic interactions with content, fellow students or teachers. Traces from these fleeting interactions have quickly disappeared or been completely hidden from all the actors.

« The abuses on personal data have made both educators and students leery of inviting “big brother” to oversee our teaching and learning »

Learning analytics gives us the optimistic promise that these interaction traces will now not only become visible, but from subsequent sophisticated analysis a host of personalized interventions, diagnostic insights, comparative intuitions and profound insights of both the strengths and the weaknesses of our education programming, will emerge. Who could not eagerly look forward to the dawn of this golden age?

But as the Canadian philosopher of communication theory Marshall McLuhan and many others have noted1, each new media takes away as well as enhances our human potential. The first challenge stems from the ownership of this data. The abuses by both governments and commercial services of personal data have made both educators and students, leery of inviting “big brother” to oversee our teaching and learning activities.

This is especially egregious when the data collected and analyzed is not made available to those who created it. As education moves beyond institutional learning management system onto a variety of personal learning tools that are selected by individual learners and teachers, the challenges of collecting data and the potential for exploitation of this personal data increases.

« Results must always be interpreted with an appreciation of their potential for inaccuracy and lack of validity »

Secondly, as researchers at Microsoft Research danah boyd and Kate Crawford noted2, learning analytics currently suffers from the delusion “that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.” The former may be true, but the later belief in “truth, objectivity and accuracy” is far from proven.

Indeed, to date we measure the easily measured — traces such as participation data, quiz results and time on line — all of which may be associated with good teaching and learning, but can as easily be associated with deception, bad pedagogy and teaching to the test. Determining and accurately measuring indicators of quality learning is a challenging task and doubtlessly influenced by both cultural and academic predisposition. Thus, results must always be interpreted with an appreciation of their potential for inaccuracy and lack of validity.

Despite these concerns, I am optimistic. Learning analytics can be a spotlight that penetrates the darkness that has to date plagued both distance educators and students. With smart application it stands to benefit both learners and teachers.



Footnotes:

1. McLuhan, M., & McLuhan, E. (1988). Laws of media: The new science (Vol. 1): University of Toronto Press, Toronto.

2. boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662-679.