Recently in my colonial American history course we discussed the “invisible worlds” of Salem, in which the persistence of superstitious belief mingled with the extreme religious fervor of the Puritans. This created a hothouse environment that may have helped foster the 1692 outbreak of witchcraft hysteria that tore that community apart and has since become the stuff of legend. In the modern world, we like to think we are too sophisticated, too logical, and too focused on proof to allow us to be so gullible to emotional arguments. We have placed our faith in science to help us make decisions about our society.
Proof of this commitment can be found in a new study that used mathematical and computer formulas to determine whether or not individual scientists will have career success. The Acuna-Allesina-Kording formula, reports The Chronicle of Higher Education, “is intended to improve upon the h-index—a tally of a researcher’s publications and citations—by adding a few more numerical measures of a scientist’s publishing history to allow for predictions of future success.” Konrad P. Kording, one of the authors of the study, took into account calculations based on how many articles a scientist has published, the time that passed after the publication of that article and the following one, and the number and kind of journals that published their work.
The resulting data, Kording argues, can then be used to determine which scientists should get more funding for their work, and help institutions direct important funding sources to scientists that “will have high impact in the future.” Universities can funnel funding to research areas and specific scientists where there is a greater chance of successful results. According to The Chronicle,
“Kording anticipates a world in which universities and grant-writing agencies work more efficiently, to the betterment of science…. And rather than punish creative adventurers who dare to tread into areas not yet recognized by other scientists, better systems of talent evaluation might mean a person with the talent to lead an intellectual revolution might actually get the money needed to do it.”
Data-Driven Determinism is Problematic
As an educator, I immediately spun a vision of the possible ways the Acuna-Allesina-Kording formula’s data-driven approach might affect all levels of education. If this approach is applied to students, it seems similar in some respects to the current emphasis on data-driven education reform. And the vision I had was not good.
Like many of those quoted in The Chronicle who are concerned about the potential uses–and abuses–of the Acuna-Allesina-Kording formula, I worry that the use of such metrics will shut off the potential for creativity, the chance that many students who are late-bloomers will have to develop their abilities later in life, and the very real possibility that these late-life “Eureka!” moments will disappear. What about all those non-traditional students who did not get a chance to develop academically earlier in their lives—do we cut off funding because their previous academic experiences do not “predict” future success?
It also may have a chilling effect on the way that students envision their futures. Check out this example from a list of 50 Famously Successful People Who Failed at First and you’ll see why:
“Einstein did not speak until he was four and did not read until he was seven, causing his teachers and parents to think he was mentally handicapped, slow and anti-social. Eventually, he was expelled from school and was refused admittance to the Zurich Polytechnic School. It might have taken him a bit longer, but most people would agree that he caught on pretty well in the end, winning the Nobel Prize and changing the face of modern physics.”
Imagine if Bill Gates had been told that he was doomed to failure because he was not an early success? Some characteristics cannot be quantified, and they may be the very characteristics that lead to success, such as perseverance and dedication.
I also think that this new use of metrics may be potentially dangerous to our democracy. For example, if we apply such calculations to student activity in the K-12 grades, and then only admit to higher education those students whose prior performance indicates the potential for future success, we will slam shut the doors of opportunity to perhaps millions who have not yet discovered their true strengths. Education should be the last place where such approaches are promoted.
I’ve already written about my concerns with data-driven education policy, and my suggestion regarding these new metrics to predict scientific success are the same: they should be one factor among many, and certainly not the most significant or decisive factor in determining where and how to invest education funds.
This is one way that we can prevent the current conventional wisdom that the only good education policy decisions and reforms are data-driven policy decisions. Like education itself, education funding and reform needs to incorporate an appreciation of and openness toward possibilities that may at first seem surprising or improbable.