UNESCO Policy Brief: Learning Analytics

As we draw to the close of an extraordinary year in the development of the Learning Analytics field, the invitation to write an executive summary on the topic is the chance to sum up where I think we’ve got to, and where we might want to be heading.

We’re at, or approaching, the peak of the hype cycle as funding flows into ‘big education data’ and every product ships with an analytics dashboard, and so inevitably we’ll be navigating the ‘trough of disillusionment’ in a few years if we don’t calibrate our expectations. So what exactly should we be aiming for?

Analytics could be disruptive simply by turbocharging student outcomes on established assessments. The explosion in educational startups signals the expanding market for giving established consumers an edge in established products. We’re talking about higher scores folks — which is great as far as it goes.

But we must ask the deeper question, do analytics build the kinds of learners that we need for our times? This is why learning analytics could and should be part of the profound challenge to the educational status quo, because we’re talking about transforming  assessment regimes deeply embedded in the formal education system at all levels. Analytics may make it possible to conduct rigorous, evidence-based, personalised, formative assessment of learning dynamics which we’ve known to be critical for deep learning, but which we’ve never been able to track and feed back — until now.

The UNESCO Institute for Information Technologies in Education publishes Policy Briefs on emerging developments in the field. Here’s my cut on the state of play, and some recommendations for institutions — although the specific examples will soon date, I think that some of the issues flagged are persistent challenges that will be with us longer. It’s been rewarding to hear that the open draft I circulated earlier for comment has already helped in framing the issues for senior university management, and here’s the update.

Summary: Learning Analytics is a rapidly growing research field and commercial , with potentially disruptive potential. While educationalresearchers have for many years used computational techniques toanalyse learner data, generate visualizations of learning dynamics,and build predictive models to test theories — for the first time, these techniques are becoming available to educators, learners and policy makers. Learning analytics promise is to transform educational research into a data-driven science, and educational institutions into organisations that make evidence-based decisions. However, critical debate is needed on the limits of computational modelling, the ethics of analytics, and the educational paradigms that learning analytics promote.