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Visual representations of biology have been widely used by scientists to understand and explain phenomena, from the representational anatomical works of Leonardo da Vinci to the theoretical phylogenetic work of Charles Darwin. We encourage students to create drawings (illustrations) as a means of better understanding details of experimental design, where data comes from, and as a means of activating the brain in ways that advance creative and critical thinking. For more information, see . 

Examples of student illustrations of experimental design used for testing the predictions of evolutionary hypotheses:

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Annotation is an important tool for students to "own" the information in tables, graphs, or text based descriptions of the world. Students write directly on graphs with the goal of making the relevant information evident in a manner that facilitates making evidence-based claims and teaching each other. 

Concept maps are explicit descriptions of knowledge and the inter-connections among key concepts. Concept maps "...provide a unique window into the way learners structure their knowledge, offering an opportunity to assess both the propositional validity and the structural complexity of the knowledge base." (Pearsall et al. 1993: 198).  

Students set up and evaluate analytical models to explore evolution. For example, students derive a simple two phenotype model (e.g. for two asexual and sexual reproduction) and explore the conditions that promote change in the characteristics of the population over time. The derived analytical model, ∆p = pqs/W, indicates that evolution is directly proportion to the amount of variation (pq) and the strength of natural selection (s) and inversely proportional to average fitness (W).

Students construct R scripts to simulate evolution or investigate the dependence of evolution on the magnitude of selection. Here is an example of a student's R script to simulation 10 generations of evolution assuming a two-fold cost of sex

#simulating evolution using a simple two phenotype model

#make a vector to store values of p
p <- rep(NA, 10)

#initialize the vector with the observed frequency of asexuals
p[1] <- 0.05

#simulate evolution
for (i in 1:9){
  W <- p[i] + (1-p[i])*0.5
  p[i + 1] <- p[i]/W
}

#plot the data
quartz()
plot(1:6, p, xlab="Generations", ylab="Frequency of asexuals", type="l", cex=2, ylim=c(0,1), xlim=c(1,10), xaxt="n")
points(1:10, p, pch=19)
axis(1, seq(1,10), seq(0,5))

Students design and illustrate experiments to test hypotheses: in this case, the hypothesis is that tail length causes the variation in reproductive success of males. The hypothesis was based on an observation of a correlation in nature. 

Collaboration involves people working together to solve problems and in the process individuals develop key skills including the ability to effectively communicate, listening for understanding, and sharing knowledge and skills that enable greater productivity than can be achieved by individuals acting alone. Importantly, collaboration builds communities.

Evidence of collaboration and community-building in EBIO:

Development of a social network in evolutionary biology during a semester (see Buchenroth-Martin 2016):