The following application of game theory is based on several assumptions. And admittedly, assumptions, like the housing market will always go up, can be dangerous. I also take a complex issue, which involves many variables, and make it simple. But my hope is that even a crude analysis can provide some insights into the problem with evaluation systems in education as they exist today. One important side note: while game theory may help explain why most people act a certain way most of the time, it certainly cannot explain why all people choose to act in the way they do. Now to the good stuff.
In game theory, a payoff matrix is used to help understand the possible outcomes that individuals or groups face
given two choices. The figure above is such a matrix. It works under the presumption that people will consider their payoffs when making decisions, which in turn helps us understand why many rational people, faced with the same set of circumstances, make the same decision. The figure above, is the payoff matrix for the choices faced by evaluators and ineffective teachers, to work hard (max effort), or to take it easy (min effort).Let us first consider the evaluator, the person who is primarily responsible for determining whether a teacher receives a satisfactory or unsatisfactory evaluation. The evaluator has two choices. The first is to exert maximum effort, and thus proceed to document and place considerable time and effort in the process of placing the teacher into the PAR system. The second choice, is to exert minimum effort. In this scenario the evaluator does not place the struggling teacher in the PAR system, but enjoys the added benefit of more time to address other issues. In the payoff matrix, we assume that a rational actor is interested in making their life simpler, and thus minimizing effort. I assign no value to exerting maximum effort, but a (+1) value to exerting minimum effort. I assign additional value to removal of the struggling teacher. If the evaluator is able to remove the teacher through placement into the PAR system, I assign a (+1) value, but if the teacher is not removed, I assign a value of zero. This leaves the following payout structure for the evaluator:
0 = Considerable effort , but no teacher removal
1 = Minimum effort, and no teacher removal
1 = Considerable effort, and teacher removal
2 = Minimum effort, and teacher removal
The ineffective teacher has slightly different payouts, but the same choices. Like the evaluator, the ineffective teacher may exert either maximum or minimum effort, but would prefer to minimize effort. Like the evaluator, I assign a (+1) value to minimizing effort and no value to maximizing effort. On the other hand, the teacher has an incentive to keep his or her job. When he or she keeps their job and thus receives scheduled step increases, I assign a value of (+1). However, because of the negative consequences associated with losing one's job, I assign a (-1) value to this outcome. This leaves the teacher with the following possible payouts:
-1= Considerable effort, and loss of job
0 = Considerable effort, and same rate of pay
0 = Minimum effort, and loss of job
1 = Minimum effort, and same rate of pay
Analysis of the payoff matrix allows us to understand why so few teachers end up dismissed from their job, and why a new system is needed that changes the payout structure for both teachers and evaluators.
Evaluators would like to remove ineffective teachers, but they understand this requires considerable effort. Let's assume evaluators don't mind this extra effort, but only if this effort pays off in the removal of the ineffective teacher. Once the evaluator places the teacher in the PAR system however, the tenured teacher has two options. The first option is to continue to place minimum effort into his or her job. However, already in the PAR system, this option will result in his or her dismissal. There is now a substantial carrot for the ineffective teacher to put in more effort in order to avoid dismissal. The result of the evaluator exerting maximum effort, is thus a payout value of zero for the evaluator, who faces a now motivated teacher willling to do what is necessary to keep their job.
Faced with the likely consequence that a teacher will not be removed even if he or she is placed in the PAR system, the rational evaluator is more likely to put forth minimum effort. In other words, the evaluator will not take the time consuming steps of putting the ineffective teacher in PAR. In this scenario, the ineffective teacher is not placed in the PAR system, but the evaluator does not have the headache associated with documenting the placement. The evaluator's life has become substantially easier. The rational ineffective teacher now has the option to either exert maximum effort to become a better teacher or to exert minimum effort, but devoid of consequences for exerting minimum effort, will likely make this choice.
In game theory, we call the above solution, the solution in which each participant acts in their own self-interest (regardless of the decision made by their "opponent"), the Nash equilibrium. In our game, this means both the evaluator and the ineffective teacher will choose to put forth minimum effort. This of course, has a negative consequence for the state of education as a whole, despite the "positive" outcome associated with the decisions made by the individual.
When teachers and administrators have incentives to do just enough to get by, something is broken. And while no one should believe all teachers are just out for survivial, we must create incentives for performances we value.
Race to the Top (RTTP) may very well give states and districts the opportunity to change this payout structure for both teachers and evaluators. Any effective evaluation system, must not only have negative consequences of failure, but positive consequences of success. And my next entry will try to explain how such a pay-off matrix will look and feel.
No comments:
Post a Comment