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Historic challenges in the biological sciences, such as the spread of disease and climate change, have created an unprecedented need for humans to engage with scientific information to address societal problems. However, understanding these socioscientific issues (SSI) can be hard due to the difficulty of comprehending their complex structures and behaviors, the intentional propagation of misinformation, and an insufficient understanding of the epistemic practices that scientists use to develop relevant knowledge. Education researchers have highlighted additional problems in the way science is taught with a focus mainly on concepts rather than practices, competing curricular mandates, and professional development activities that do not provide usable knowledge. The research reported here follows more than a decade of work using agent-based computational models to support the comprehension and analysis of complex biological systems. Our recent work has aimed to build tools and strategies to support students in decision making about complex SSIs. In this paper, we discuss 7 design challenges and principles that underpin this recent focus. Specifically, we combine agent-based modeling with strategies to develop students’ epistemic performance in high school biology curricula. We then provide a detailed case study of how the 7 design principles were used to create a disease epidemic model and unit anchored in the biology topic of the nature of science. Our goal is to offer a comprehensive set of research-derived design principles that can bridge classroom experiences in biology to applications of SSIs. 

One critical step in the challenging process of curricular reform is determining how closely a curriculum aligns with national recommendations. Here, we examine the alignment of teaching, assessment, and student experience in undergraduate biology courses with the Vision and Change core competency recommendations. We applied the intended–enacted–experienced curriculum model to obtain a more complete, multiperspective view of the curriculum. First, we developed and piloted the BioSkills Curriculum Survey with more than 100 biology instructors across five institutions. Using multilevel logistic regression modeling of the survey data, we found that instructors were equally likely to report teaching all competencies; however, they reported assessing some competencies more than others. After adding course characteristics to our model, we found that the likelihood of teaching certain competencies depended on course type. Next, we analyzed class materials and student perceptions of instruction in 10 biology courses in one department. Within this smaller sample, we found that instructors messaged a narrower range of competency learning outcomes on their syllabi than they reported teaching on the survey. Finally, modeling revealed that inclusion of an outcome on assessments, but not syllabi, increased the likelihood that students and their instructor agreed whether it was taught. 

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. 

In the laboratory-based disciplines, selection of a principal investigator (PI) and research laboratory (lab) indelibly shapes doctoral students’ experiences and educational outcomes. Framed by the theoretical concept of person–environment fit from within a socialization model, we use an inductive, qualitative approach to explore how a sample of 42 early-stage doctoral students enrolled in biological sciences programs made decisions about fitting with a PI and within a lab. Results illuminated a complex array of factors that students considered in selecting a PI, including PI relationship, mentoring style, and professional stability. Further, with regard to students’ lab selection, peers and research projects played an important role. Students actively conceptualized trade-offs among various dimensions of fit. Our findings also revealed cases in which students did not secure a position in their first (or second) choice labs and had to consider their potential fit with suboptimal placements (in terms of their initial assessments). Thus, these students weighted different factors of fit against the reality of needing to secure financial support to continue in their doctoral programs. We conclude by presenting and framing implications for students, PIs, and doctoral programs, and recommend providing transparency and candor around the PI and lab selection processes.