Page 34 - Campus Technology, March/April 2018

C-Level View
customers, that will want us to continue to believe in the power of the “single stack,” I think many of us providing services in the land of education technology are going to turn to APIs to mix and match different kinds of ed tech solutions, so that stakeholders can customize the platform services they provide to their users. This is true whether one is a commercial provider of platform services or running a data center on a campus. Flexibility and responsiveness will continue to be key.
CT: Where can we go from here — in a general sense — using predictive analytics for the benefit of education? And what do we need to be thinking about now?
Wagner: [Data scientist] William Vorhies wrote a provocative blog post a few months ago in which he asserted that
it was time for predictive analytics to take a victory lap. It’s true that people no longer immediately assert that predictive
analytics are the devil’s business when talking about education research. Still, as recently as 2015 I was asked by graduate students in education disciplines to
join their dissertation committees as an external reviewer, to make sure that at least one person on their committee viewed predictive analytics as a legitimate research methodology for a dissertation in education. Old habits die hard in the academy.
Nevertheless, the recognition that data scientists are bringing predictive analytics, machine learning, deep learning and arti- ficial intelligence to the table has silenced some of the loudest objections, almost
to the point where education research-
ers now worry about losing their research voice as their methods are trumped by data science’s more mathematically ori- ented models. This is a topic we discussed at some length at last year’s Research Symposium at the Distance Teaching & Learning Conference in Madison.
These days, many of us in education are finding ourselves held in thrall by the sirens
of deep learning and artificial intelligence.
I think part of the allure is the hope that perhaps the secret to analytical success will be contained in increasingly complex and sophisticated findings — more than in what has been uncovered to date. It’s sort of like looking for data DNA. I suspect this also speaks to the rapidly emerging field of learning engineering, but that is an entirely different conversation.
What all this is really pointing to is that
we are pretty much done with the so-called “awareness phase” of predictive analytics. These days we find ourselves in the midst of exploration and adoption, whether we like it or not. With that comes the first wave of education transformations from having insight and information about how our prac- tices affect student and faculty performanc- es, processes and learning outcomes.
For a real transformation to succeed, we first need to understand the problems we want to solve and the opportunities we want to realize. Next, we need a
plan, with clearly stated goals and the
commitment to see the plan through to its implementation. We need to know: How do these technologies make a difference? How much? For whom? When? Without that, we’ve got nothing more than new technology, with little opportunity to show whether it is working or not — not exactly the transformation your provost is going to be looking for at the end of the year.
Then finally, we can put that plan to work and drive true transformational value. But we need to stay grounded: We need to keep our eyes on our true values as educa- tion stakeholders and remember why we “do” education at all.
More than ever before, I know that student success is not software, and that predictive analytics is not punitive if used ethically and responsibly. Student success is the shared value construct of helping our citizens achieve their dreams of a better
life for themselves and their families. Any
of us who care about the future of our communities and our world should be able to get behind that.
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CAMPUS TECHNOLOGY | March/April 2018