entire lab period. It also succeeds in getting students working
together and interacting throughout the learning process.
Homework Assignment & Follow-up
As homework, each student plots the mean and standard error of
speed for each prey and predator generation in their simulation.
They transcribe data from their opposing team during lab and
graph both results together. This makes use of in-lab calculations
and employs graphing skills introduced during previous lab assignments completed earlier in the semester.
Students see several trends in their graphs (Figure 1), which we
discuss as a class in the subsequent lab period. First, they see coevolution in the parallel increase in speed for both populations (Tatina,
2007). They also see shrinking genetic diversity in the standard
error, and they see the limitation that the traits of individuals within
the population set on the potential to increase population speed.
They further see that a beneficial mutation has a limited immediate
population-level effect but influences subsequent generations. In
the case shown for predators (Tables 2 and 3), strong selection eliminated all speeds below 7, demonstrating negative or “purifying”
selection (Loewe, 2008). In addition, the beneficial mutation in the
third generation of the prey population allowed mean speed to
exceed 7—the maximum speed of the predator population—by
the fifth generation. Overall, the game and accompanying calculations clearly illustrate how natural selection and beneficial mutation
are separate but interacting mechanisms of evolution.
For further class discussion, the data and analyses demonstrate
natural selection as a process of sorting among varied individuals.
For instance, students quickly see that selection pressure favors faster
individuals, especially as selection eliminates the slowest predators,
which cannot encounter two catchable prey (Table 2). Students also
see that adaptation and coevolution change population averages
through differential survival, but that natural selection is not “all or
nothing” because a predator with average speed sometimes encounters slower prey. Further, students can see that although natural selection affects the reproductive success of individuals, the results of
natural selection become evident across subsequent generations.
If desired, students can calculate survival probabilities for dif-
ferent speeds or track the changing proportions of specific speeds
across generations. Calculating the percentage of each number sur-
viving each generation illustrates higher survival for faster individ-
uals ( i.e., higher numbers) and that for individuals having average
speed in the starting population, survival decreases by generation,
as the overall population gets faster. Similarly, calculating the per-
centage of the population comprised by each number, by genera-
tion, illustrates the disappearance of low numbers, the decrease of
average numbers, and the increase of high numbers. Students can
tabulate or graph these results to see the trends, which are also
excellent fodder for in-depth class discussions.
Assessment of Student Learning
To assess student learning, we administered pretests and posttests,
available to students online in the week preceding and the week
following simulations, respectively. These tests were voluntary
and received approval from the Institutional Review Board of
Weber State University (protocol nos. 2016-COS-3 and 2017-
COS-6) prior to implementation. We asked students to complete
an informed-consent form about this study before responding to
the pretest. We excluded students from the analysis if they elected
not to participate in the study. We also excluded students who only
responded to either the pretest or posttest. With these caveats, we
accumulated data for 298 students from 18 lab sections over four
For the assessment, we used the same questions in pretests and
posttests within each semester and for all lab sections. We held
nine multiple-choice questions constant across all semesters for
long-term analysis of student learning. Paired t-tests using these
long-term data indicated that posttest scores exceeded pretest
scores each semester, with average improvement from 11% to
13.8%, depending on semester (Table 4).
To discern the simulation’s relative effectiveness for students
with different levels of prior understanding, we classified students
into one of three groups based on pretest scores: (1) ≤50%, (2)
51–79%, and (3) 80–99% (excluding students earning 100%).
Paired t-tests showed significantly higher posttest scores for all
three groups (Table 5). Students in the first group showed greatest
improvement (~27%), possibly reflecting higher potential for
improvement. It is nevertheless encouraging both that the simulation most benefited students with poorest prior understanding
Table 4. Paired t-test comparisons of students’ overall performance (percentage) on pretests and
posttests, by semester.
Fall 2016 Spring 2017 Fall 2017 Spring 2018
(X̅ ± SE)
77±2 66±2 67±2 71±2
(X̅ ± ± SE)
91±1 77±3 80±3 84±2
n 107 55 65 71
t 8.878 3.786 5.504 7.439
p <0.001 <0.001 <0.001 <0.001
Cohen’s d 0.858 0.511 0.683 0.883