Liquid modernity & learning analytics: On educational data in the 21st century

I was recently interviewed for a (forthcoming) piece in eLearn Magazine.  Below are my responses to a couple of key questions, reproduced here in their entirety.


eLearn: You have a Ph.D. in Philosophy. Could you share with us a little about your history and your work with learning analytics?

TH: What drives me in my capacity of a philosopher and social theorist is an interest in how changes in information technology affect how we think about society, and in the implications our changing conceptions of society have on the role of education.

I think about how the rapid increase in our access to information as a result of the internet has led to the advent of what Zygmunt Bauman has called ‘liquid modernity.’ In contrast to the world as recently as a half century ago — a world defined by hard and fast divisions of labor, career tracks, class distinctions, power hierarchies, and relationships — the world we live in now is far more fluid: relationships are unstable, changes in job and career are rapid, and the rate of technology change is increasing exponentially. The kind of training that made sense in the 1950’s not only doesn’t work, but it renders students ill-prepared to survive, let alone thrive, in the 21st century.

When I think about our liquid modern world, I am comforted to know that this is not the first time we have lived in a world of constant change.  We experienced it in Ancient Greece, and we experienced it during the Renaissance.  In both of these periods, the role of the teacher was incredibly important.  The Sophists were teachers.  So were the Humanists.  For both of these groups, the task of education was to train citizens to survive and thrive under conditions of constant change by cultivating ingenuity, or the ability to mobilize a variety of disparate elements to solve specific problems in the here and now.  For them, education was less about training than it was about cultivating the imagination, and encouraging the development of a kind of practical wisdom that could only be gained through experience.

It is common among people on analytics circles to use a quote apocryphally attributed to Peter Drucker: “What gets measured gets managed.” Indeed, when we look at the history of analytics, we can find its origins in the modern period immediately following industrialization, concerned with optimizing efficiency through standardization and specialization.  Something that has worried me is whether or not there is a mismatch between analytics – an approach to measurement with roots in early modernity – and the demands of education in the 21st century, when students don’t need to be managed, so much as prepared to adapt.

Is learning analytics compatible with 21st century education?

I believe the answer is yes, but it requires us to think carefully about what data mean, and the ways in which data are exposed.  In essence, it means appreciating the analytics do not represent an objective source of truth.  They are not a replacement for human judgment.  Rather, they represent important artifacts that need to be considered along with a variety of other sources of knowledge (including the wisdom that comes from experience) in order to solve particular problems here and now.  In this, I am really excited about the kind of reflective approaches to learning analytics being explored and championed by people like John Fritz, Alyssa Wise, Bodong Chen, Simon Buckingham Shum, Andrew Gibson, and others

eLearn: You wrote in an article for Blackboard Blog that “analytics take place at the intersection of information and human wisdom”. What does it mean to consider humanistic values when dealing with data? Why is it important?

TH: I mean this in two ways.  On the one hand, analytics is nothing more and nothing less than the visual display of quantitative information.  The movement from activity, to capturing that activity in the form of data, to transforming that data into information, to its visual display in the form of tables, charts, and graphs involves human judgment at every stage.  As an interpretive activity, the visual display of quantitative information involves decisions about what is important.  But it is also a rhetorical activity, designed to support particular kinds of decision in particular kinds of ways.  Analytics is a form of communication.  It is not neutral, and always embeds sets of particular values.  Hence, it is incumbent upon researchers, practitioners, and educational technology vendors to be thoughtful about the values that they bring to bear on their analytics, and also to be transparent about those values so that they can inform the interpretation of analytics by others.

On the other hand, to the extent that analytics are designed to support human decision-making, they are not a replacement for human judgment.  They are an important form of information, but they still need to be interpreted.  The most effective institutions are those with experiences and prudent practitioners who can carefully consider the data within the context of  deep knowledge and experience about students, institutional practices, cultural factors, and other things.

As artifact, analytics is the result of meaning-making, and it informs meaning-making.

eLearn: Do you think that institutions are already taking advantage of all the benefits that learning analytics can offer? What are their main challenges?

TH: No.  The field of learning analytics is really only six years old. We began with access to data and a sense of inflated expectation.

The initial excitement and sense of inflated expectation actually represents a significant challenge.  In those early days, institutions, organizations, and vendors alike promise and expected a lot.  But no one really knew what they had, or what was reasonable to expect.

Mike Sharkey and I recently wrote a series of pieces for EDUCAUSE and Next Generation Learning on the analytics hype cycle, in which we argued that we have entered the trough of disillusionment and have begun to ascend the slope of enlightenment (see HERE & HERE).  Many early adopter institutions were excited, invested, and were hurt. We are at an exciting moment right now because institutions, media, and vendors are beginning to develop far more realistic expectations. We know more, and can now start getting stuff done.

Another major challenge is adoption.  It’s easy to buy a technology.  It’s harder to get people to use it, and even harder to get people to use it effectively.  Overcoming the  adoption challenge is one that involves strong leadership, good marketing, and excellent faculty development.  It also requires courage.  Change is hard, and initially even the most successful institutions encountered significant flak.  But what we see time and time again that a well-executed adoption plan that emphasizes value while assuring safety (should never be punitive) very quickly overcomes negativity and sees broad-based success.

Lastly, a major challenge that institutions have is being overwhelmed by the data, and losing sight of the questions and challenges they what to address.  It is important to invest in data access so that you have the material you need to understand and address barriers when they arise, but questions should come first.