I am fascinated by learning analytics and what it can, and can't, answer for higher education. A key concern, for me, is asking questions of our data that lead to meaningful answers. I don't care about the average time on task for students interacting with an online module if it doesn't lead to a meaningful understanding of how students learn that topic or a meaningful intervention for students who are struggling. I am committed to understanding "why" and always want to know "what can I do now that I know this?"
So I was taken aback by this article sent to me by a higher education colleague. The focus is on business but it touches on the same issues we have with big data in higher education. Big data is big, really big, and it takes considerable time and expertise to sort, understand, and find meaning. The author argues that it is possible to apply "analytics to massive, detailed data sources to identify what works without having to worry about why it worked." (emphasis in the original). Can this be true for higher education? Can we glean meaningful insight into what works without understanding how it works?
Higher education, unlike a point of sales transaction, has a long tail. Some of the why's we deal with when looking at success in college might have more to do with prior preparation, previous experience (or lack of experience) in the subject matter, etc etc. We can't change some of those factors, which is why this article has me thinking. If we can glean a best practice from data only, from seeing exactly what works (most of the time) and never understand why, does that matter? Is the "why" a distraction? I am not convinced it is - ultimately, for education policy, it is important to know what kinds of k-12 education experiences correlate to success in higher education, e.g. But maybe, in the medium term, it is okay to leave the "why" aside.