and that almost all students gained some benefit. We are confident
that students also benefited from completing the calculations and
working in teams.
Importantly, after the simulation, students better distinguished
natural selection from mutation (Table 6). Over three semesters,
we asked the additional question “If no mutation occurs to affect
the running speed, what will most likely happen to the running
speed of predator and prey populations over time?” In the pretest,
only 18–28% chose the correct answer, “The average running speed
of both predator and prey populations will first increase and then
remain the same.” Many chose “The average running speed of predator and prey populations will never change due to lack of mutation.” Encouragingly, 49–59% (depending on semester) identified
the correct answer in the posttest. A chi-square goodness-of-fit comparison indicated that improvements were statistically significant
every semester (Table 6).
In summary, this simulation exposes mechanisms of natural selec-
tion that are commonly misunderstood. It also allows students to
work as teams, practice and apply quantitative skills, and draw their
own conclusions. Ideally, an active-learning exercise that distin-
guishes genotype from phenotype by calculating separate allele
and phenotype frequencies (e.g., Lee et al., 2017; Jördens et al.,
2018) would follow this simulation. However, we find that this sim-
ulation is ideal for initial exposure to the theory of natural selection.
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Table 5. Paired t-test comparisons of percentage improvement on pretests and posttests for students
grouped by level of prior understanding, based on pretest scores (excludes students earning 100% on the
Groups (Pretest Percentage
(X̅ ± SE)
44±1 65±1 84±1
(X̅ ± SE)
71±3 80±2 92±1
n 51 113 108
t 7.952 9.969 8.252
p <0.001 <0.001 <0.001
Cohen’s d 1.114 0.938 0.794
Table 6. Chi-square goodness-of-fit comparison, by semester, between pretest and posttest percentages
of students correctly responding to the question “If no mutation occurs to affect the running speed, what
will most likely happen to the running speed of predator and prey populations over time?”
Spring 2017 Fall 2017 Spring 2018
Pretest percent 18 28 23
Posttest percent 49 52 59
n 55 65 71
χ2 11.77 8.21 19.7
p <0.001 0.004 <0.001
Cramer’s V 0.462 0.355 0.527