After being forced to miss the final session yesterday (sorry Dan), I’m back at it at the #masonfuture conference. The day began with some tentative summary of Day 1 by President Cabrera, who charged the room with continuing to think about “preconceived notions we came with that we are now questioning.”
One of the notions that I came with that I continue to question is the degree to which universities like ours can find our way into the future with our current staffing patterns. As a recent report by Ernst and Young argues, universities in Australia cannot survive to 2025 with their current business models. A big problem, the authors argue, is that all the universities in Australia (and I suspect in the U.S. as well) have become too staff heavy and will need to rebalance their staffing patterns if they are going to become more nimble in coming decades. The authors argue that faculty represent income centers, while staff represent cost centers, and that unless these two are brought into balance, universities are in big trouble.
Given the clear interest in pushing online education coming out of the rhetoric of this conference, universities like Mason are going to have to take a long, hard look at how we might implement these sorts of sweeping changes without a significant addition of staff to make it possible. Online is not even remotely frictionless, and staffing the effort will be very, very expensive.
Will we add staff? Or will we repurpose existing staff? We don’t have the money for the former, and if we do the latter, what things will those staff stop doing. This is a conversation we are not having at this conference and I’ll be interested to see if we do.
Our morning plenary speaker was Suzanne Walsh of the Gates Foundation who began with a question: How might we use data to make better and different decisions? A key part of her argument is that universities are not doing a good job mining data about their students to maximize institutional success. In this she promoted the work of Civitas Learning and their approach to using data to help universities to make better decisions about enrollment and retention.
For example, one of the things she talked about was using data to identify courses that promote or hinder student retention. I’m sure that some of what she described seemed “new” to some of the people in the room. Alas, what she was describing really isn’t new at all. In my former life I was an enrollment management consultant and we were doing this sort of thing in the late 1980s with our clients–using good old fashioned main frame computers and good old fashioned multiple regression analysis.
The only thing that seemed “new” here was the very nice visual displays of those data. It’s certainly much easier to see connections in the data if the connected data are all purple or red, or are scaled to point to those that seem more interesting than others. But, as Edward Tufte has been grumping for decades, these sorts of applications often violate the optimal “data ink ratio” and obscure more data than they display.
I’m not disputing her larger point that we need to do a much better job of using data to make decisions on our campuses. And, don’t get me wrong, I love a good data visualization. But we already have the data she’s arguing for and plenty of very qualified statisticians, economists, policy analysts, and others who can analyse our data in some very useful ways.
The point she made that is more useful, is that we have to be open to the use of data, as opposed to instinct, conventional wisdom, or urban legends about “what our students are like” to make decisions. So, to take my earlier example about staff/faculty balance, I wonder what our data would tell us about the 10 or 15 year trend at Mason relative to that balance and how whatever decisions we have made about that balance have helped us or hindered us from achieving the goals we’ve set for ourselves.
This sort of question (and the willingness to be open to whatever the data say) is especially important at our institutions like ours, not only because of the questions we face going forward from 2012, but also because we are in the midst of writing a new strategic plan for that “going forward.” I agree completely with Walsh’s point that we need to let those data tell us what they say, not what we want them to say, and then use those data to help us with the decisions we need to make. But we also need to remember that data are not all. Intuition and institutional memory matter too.