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Using analogies is a standard practice for both teaching and communicating ideas in science. Here we upend the traditional lesson, where the instructor provides a fully constructed analogy and explains it, by having the students develop a complex analogy themselves. This high engagement, peer learning activity engages students in critical thinking and analogical reasoning to foster deeper understanding of molecular processes and their interconnection. In this lesson, groups of students are asked to relate given items to DNA and to decide which level it best represents (nucleotide, gene, chromosome, or genome). Next they are tasked with extending the analogy to include other actors in the central dogma of molecular biology (RNA, protein, polymerases, ribosomes, etc.), and then to extend it even further (introns/exons, mutations, evolution, etc.). Finally, each group presents their analogy to the class, and they evaluate each other. We provide multiple examples of items that can be used in the activity, but others can be identified with some creativity. This exercise is also an excellent tool for instructors to discover where their students have gaps and need help making connections to bridge their understanding of processes in molecular biology. 

The General Biology–Measuring Achievement and Progression in Science (GenBio-MAPS) assessment measures student understanding of the Vision and Change core concepts at the beginning, middle, and end of undergraduate biology degree programs. Assessment coordinators typically administer this instrument as a low-stakes assignment for which students receive participation credit. While these conditions can elicit high participation rates, it remains unclear how to best measure and account for potential variation in the amount of effort students give to the assessment. To better understand student test-taking motivation, we analyzed GenBio-MAPS data from more than 8000 students at 20 institutions. While the majority of students give acceptable effort, some students exhibited behaviors associated with low motivation, such as low self-reported effort, short test completion time, and high levels of rapid-selection behavior on test questions. Standard least-squares regression models revealed that students’ self-reported effort predicts their observable time-based behaviors and that these motivation indices predict students’ GenBio-MAPS scores. Furthermore, we observed that test-taking behaviors and performance change as students progress through the assessment. We provide recommendations for identifying and filtering out data from students with low test-taking motivation so that the filtered data set better represents student understanding.