Several years ago, a group of us wrote a concept paper on Open Learning Analytics (.pdf). Our goal was to create openness as a foundation for the use of data and analytics in education. We have, it appears, largely failed to have our vision take root.
Few things are more important in education today than the development of an open platform for analytics of learning data. It’s a data-centric world. Data, and the analysis of those data, are a rapidly emerging economic value layer. Most educators and students are unaware of how much algorithmic sorting happens in the educational process. Even before students apply to a university, the sorting has started (postal/ZIP codes can indicate chances of success). Recommender systems suggest next courses. Engagement with course content produces predictive models. Suggested help resources are generated for students identified to be at risk. And this all happens behind the scenes as the Wizard of Algorithms spins dials and outputs intimidating results (often with more smoke and noise than actual usefulness) that are starting to drive learning practices that cover the full range of a student’s engagement with higher education.
We are, as a field, facing an interesting time. The decisions that we make now will cast a long shadow into the future. And the best decision, in uncertain times, is the one that allows the greatest range of decisions in the future. It is here, in analytics and data use in education, that far more attention and awareness is needed than is currently evident. Algorithms will subsume most of our educational practices as they will embody certain pedagogies, support roles, and even faculty practices. Quite simply, the shape of tomorrow’s university is now actively being coded into analytics models. I’m generally fine with this as a concept, but quite nervous about this as an action. The future needs to be open. And yet, the exact opposite is happening.
The article in the Chronicle today on Big Data and Education is timely reminder of the importance of the work and the challenges of a closed learning analytics future. The work is rather urgent. And we as academics have been sleeping.