Computer models have several features that help in teaching the dynamics of basic concepts in population genetics. Although there is no substitute for a firm understanding of the basic underlying quantitative features of the Hardy-Weinberg equilibrium and the resulting effects of violations of its assumptions, it can be very difficult to examine the long-term dynamics of population genetic processes with only a pencil-and-paper approach. It is also practically impossible to experimentally examine such biological processes in the typical college-level course with the usual limitations of time and resources. On the other hand, it is very important for students to understand the relationship between the process of microevolution at the population level and the changes in gene pools caused by violations of the Hardy-Weinberg assumptions. Computer models that demonstrate the long-term dynamics of population genetic processes can be very valuable to instructors in this regard.
Perhaps more than any other discipline within biology, evolution is conceptual in nature and cannot be mastered by rote. Students must have a strong working understanding of the concepts to be able to apply them. For example, a student might memorize that gene flow and population size affect the possibility of genetic drift in a population, but unless students understand both concepts and how they interact, they will not be able to analyze whether or not significant changes in allele frequencies are to be expected in a given scenario. In fact, evolution is identified as one of the most misunderstood and difficult concepts to teach in biology (Bishop & Anderson, 1990; Ferrari & Chi, 1998).
Citing examples and case studies in lecture is useful when teaching evolutionary concepts, but this does not require students to engage directly with the material. By contrast, conducting experiments in laboratories challenges students to formulate hypotheses and make predictions based on theory, which facilitates conceptual learning. The problem, unfortunately, is that realistic evolutionary experiments typically take much longer than a semester or academic year. As a result, it has been difficult to develop practical educational analogues for most classic experiments in lab and classroom settings that foster an understanding of theory through empirical demonstrations.
Although they are by no means replacements for hands-on laboratory or field experience, computer simulations provide a useful tool for illustrating and conveying complex concepts in evolution (Soderberg & Price, 2003; Latham & Scully, 2008; Perry et al., 2008). In the most realistic simulations, the interactions among individual organisms are modeled with elements of probability and randomness. The interactions of simulated individuals (agents) generate results that are affected by variables in a probabilistic, rather than deterministic, way. In other words, agent-based simulation models create a virtual world in which experiments can be conducted. Moreover, because of the built-in stochasticity, such simulations generate data that are much more biologically realistic and interesting than deterministic models that generate results from mathematical formulae.
In the virtual world, students can quickly conduct experiments that would take years or even decades in the real world. By working with simulations, students are obliged to interact with the concepts being illustrated. They cannot be passive. Students learn the effects of different variables by tweaking them and observing the results. Furthermore, students will see that biological results are not as neat and tidy as the graphs typically presented in textbooks. Rather, real biological results are messy, and trends in the data are revealed by statistical analyses.
The freeware models available now are limited in their usefulness for describing microevolution in some significant aspects. …