Guidelines for using big data to improve colleges

A group of researchers and other interested parties recently made suggestions about how big data from higher ed can be used for good within higher ed:

To Stevens and others, this massive data is full of promise –­­ but also peril. The researchers talk excitedly about big data helping higher education discover its Holy Grail: learning that is so deeply personalized that it both keeps struggling students from dropping out and pushes star performers to excel…

The guidelines center on four core ideas. The first calls on all players in higher education, including students and vendors, to recognize that data collection is a joint venture with clearly defined goals and limits. The second states that students be told how their data are collected and analyzed, and be allowed to appeal what they see as misinformation. The third emphasizes that schools have an obligation to use data-driven insights to improve their teaching. And the fourth establishes that education is about opening up opportunities for students, not closing them.

While numbers one and two deal with handling the data, numbers three and four discuss the purposes: will the data actually help students in the long run? Such data could serve a lot of interested parties: faculty, administrators, alumni, donors, governments, accreditation groups, and others. I suspect faculty would be worried that administrators would try to squeeze more efficiencies out of the college, donors might want to see what exactly is going on at college, the government could set new regulatory guidelines, etc.

Yet, big data doesn’t necessarily provide quick answers to these purposes even as it might provide insights into broader patterns. Take improving teaching: there is a lot of disagreement over this topic. Or, opening opportunities for students: which ones? Who chooses which options students should have?

One takeaway: big data offers much potential to see new patterns and give decision makers better tools. However, it does not guarantee better or worse outcomes; it can be used well or misused like any sense of data. I like the idea of getting out ahead of the data to set some common guidelines but I imagine it will take some time to work out best practices.

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