Jul 24

Ryerson University v. Ryerson Faculty Association: Arbitrator Forbids Use of Teaching Evaluations in Hiring Decisions, Toronto Employment Lawyer


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Most universities allow students to evaluate their professors, often at the end of the year, and the universities rely on these evaluations when awarding promotions. The use of student evaluations has always been controversial, and faculty associations have long argued that the evaluations do not accurately measure teaching ability. The faculty association (“the RFA”) and the University agreed to refer the issue surrounding these evaluations to arbitration. Now, in Ryerson University v Ryerson Faculty Association, 2018 CanLII 58446 (ONLA), the arbitrator has forbidden the University from using student evaluations.

The University’s position was that student evaluations are a valuable if imperfect source of information. The RFA disagreed, arguing that, in addition to being methodologically flawed, student evaluations are likely biased against women and racial minorities. The RFA introduced evidence suggesting that women and racial minorities tend to perform poorly when compared to their male and white peers. The University did not challenge the RFA’s evidence, instead arguing that student evaluations are a tool used throughout the post-secondary education sector, and are valuable despite their flaws.

The arbitrator held that “the ubiquity of the [student evaluation] tool is not a justification, in light of its potential impact, for its continuation, or for mere tinkering. The evidence is dispositive that some of the questions do not elicit any useful information about teaching effectiveness and are subject to bias.” In conclusion, he ordered the use of student evaluations struck from the collective agreement.

While this is decision is merely directly relevant Universities, in applying similar logic more broadly this decision highlights the willingness of arbitrators and courts to look at whether the employer’s chosen method of performance evaluation is actually reasonable, effective and unbiased.

Data-based hiring decisions are generally good, but they need to be done correctly and cognizant of any bias. The employer needs to be able to demonstrate that every evaluation is 1) methodologically sound and 2) directly connected to an actual job requirement.

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