Wednesday, August 14, 2013
Data and Debate
Shared governance requires shared access to Big Data.
One positive move at my college is that the faculty may have that access sooner rather than later. If that happens, the set of facts being considered will be broader. You might get better buy-in on that example you gave about what "works" if the vocal defenders were expected to test their ideas against Big Data rather than just listen to yours.
I see this as important because those mandates you mention leave the identification of outcomes and their assessment to the collective us at present. Define the outcome first (whether for a class or the staff roles you mentioned a few days ago), including the operational definition set by how you agree to assess it. Each answer will generate new questions that is at the heart and soul of shared governance.
I love that idea of Data Day, but it would be even better if faculty could identify gaps and have them filled before a reprise session a day or a month or a semester later.
So a natural question to ask when confronted with a pile of data is to be somewhat skeptical. Who gathered this data? Do they have some sort of agenda?
In addition, is there some motive for the people who are supplying the data to lie to the people who are gathering the data? If I report the “wrong” numbers to the reviewers, can I end up looking bad? Can I be dinged by the administration if I don’t meet some set of prior-designated numbers? Maybe I should make sure that I always report numbers that please the powers that be.
It might be sort of like the Five Year Plans in the old Soviet Union, where everyone lied to everyone else all the way up the chain, so much so that the Politburo made decisions based on numbers that almost everyone knew were entirely fake.
1) As others have said, access to data is not always egalitarian. The gatekeepers of the data answer to people, and I might have a story or two about that...
2) Limited data doesn't necessarily paint a complete picture. I find it fascinating that people can be so easily impressed by some small data set that actually tells us very little.
3) If data is incomplete, it's OK to have a Bayesian prior. I'm open to having my mind changed with data, but until I see adequate data I'm also OK with defaulting to common sense and personal experience. Lack of data should not be a recipe for paralysis, it should just be grounds for caution before making expensive or irreversible decisions.
4) Requests for data can be turned into power plays. I was once in a committee meeting where an administrator wanted us to approve a certain project. We knew that similar projects had been started and abandoned at other campuses in the same state system. So I asked her if she knew why these projects had failed to take off at peer institutions. Her response was that she had no idea why, and we need to look at data before we assume that her remarkably similar proposal would fail.
Um, no. The default should be that when something has failed repeatedly, the data-collection burden should be on the person who wants to try it one more time, not on the person who says "Hey, wait, this has failed every time it's been tried!" However, this administrator is far more slick than me, so somehow her response is "I don't know the answer to your questions, so the burden is on you to answer it."
"How does [shared governance] work when a faculty is majority adjunct?"
First, how do you count adjuncts? Is it by pure numbers regardless of load, or is it by sections taught? At my CC, we are close to 50% of sections taught, but there are many more adjuncts than full-time faculty. The fraction also depends on whether and how you count lab classes.
(If you counted all the grad students, my grad school was majority adjunct 40 years ago.)
Second, how do you do governance? Is it a representative body or does it consist of every faculty member? Are adjuncts members?
(When I was in grad school, graduate students had voting representation on all major university shared governance bodies. Always a minority, but always a voice.)