A renaissance in the science of learning has led psychologists to uncover strategies that reliably improve student memory and testing performance. Among those strategies are: spacing out study items over time, taking practice tests and self-explanation of course material. Yet, as a student, you are unlikely to include any of the above in the list of qualities you look for in a class. Rather, as the research has shown, you are most likely to seek out a class with an enthusiastic instructor.

As PBS director of undergraduate instruction Ben Motz observes, enthusiasm is considered “the hallmark of good teaching” and typically leads to good evaluations and higher enrollment. Teachers are therefore taught to convey enthusiasm. Yet, he adds, “There’s never been a clear sequence of experiments to show the effect of enthusiasm on student learning. Not without mixed results.”

So, does enthusiasm actually improve student learning? Do students learn more with enthusiastic instructors, as opposed to, say, a bland and neutral one, who nevertheless uses rigorous tactics to improve student achievement?

This is what Motz and his colleagues in the science of teaching and learning set out to explore – and they took up Kruschke’s approach to Bayesian statistics to do so.

In a carefully controlled experiment, Motz and his colleagues tested whether students would perform better at a memory task if given instructions by an enthusiastic instructor than by a neutral one. They also wanted to know whether either teaching style could be supplemented with particular teaching practices to improve student learning – a spacing technique (in which items are learned at spaced intervals) known to improve student learning and a massed technique (in which items are learned back- to-back) that is less effective.

Bayesian or frequentist, their outcome would have been the same: Spacing techniques, as expected, improved performance, but enthusiastic instruction did not. However, the Bayesian approach enabled the researchers to develop a much more informative analysis that would convey the study’s implications for student learning and future research. The results were published in the July 2017 issue of PLOS ONE.

As Motz explains, “We could have done what we consider traditional stats, where the main goal is to make a binary decision: Is there an effect of enthusiasm? But using Kruschke’s approach to Bayesian estimation, we were able to directly infer how much benefit students get from an enthusiastic instructor, making apples-to-apples comparisons with the benefit of spaced study.” In this case, where frequentist methods would have given limited information about the effect of enthusiasm, Bayesian statistical analyses painted a more detailed picture.

So, do we want to curb our enthusiasm for enthusiastic instruction? Maybe not so fast. But we should perhaps reallocate this enthusiasm, as a Bayesian might say, to Bayesian statistics and the science of learning.