A “Rule-of-Five” Framework for Models and Modeling to Unify Mathematicians and Biologists and Improve Student Learning

Abstract

Despite widespread calls for the incorporation of mathematical modeling into the undergraduate biology curriculum, there is lack of a common understanding around the definition of modeling, which inhibits progress. In this paper, we extend the "Rule-of-Four," initially used in calculus reform efforts, to a "Rule-of-Five" framework for models and modeling that is inclusive of varying disciplinary definitions of each. This unifying framework allows us to both build on strengths that each discipline and its students bring, but also identify gaps in modeling activities practiced by each discipline. We also discuss benefits to student learning and interdisciplinary collaboration.

BioSkills Guide: Development and National Validation of a Tool for Interpreting theVision and ChangeCore Competencies

“…BioSkills Guide responsible conduct of research (Diaz-Martinez et al, 2019), quantitative reasoning (Durán and Marshall, 2018;Stanhope et al, 2017), bioinformatics (Wilson Sayres et al, 2018), data science (Kjelvik and Schultheis, 2019), data communication (Angra and Gardner, 2016), modeling (Quillin and Thomas, 2015;Diaz Eaton et al, 2019), the interdisciplinary nature of science (Tripp and Shortlidge, 2019), and scientific writing (Timmerman et al, 2011). Efforts to define general or STEMwide educational goals for college graduates can also inform how we teach competencies in biology, such as the Association of American College and University VALUE rubrics (Rhodes, 2010) and more targeted work on information literacy (Association of College and Research Libraries, 2015), communication (Mercer-Mapstone and Kuchel, 2017), and process skills (Understanding Science, 2016;Cole et al, 2018).…”

BioSkills Guide: Development and National Validation of a Tool for Interpreting the Vision and Change Core Competencies

“…Varying definitions of models were a common theme in survey comments and interviews. Recently a group of mathematicians and biologists (NIMBioS) joined forces to address this issue (Diaz Eaton et al, 2019). They argue that differences in conceptions of modeling among scientists within and across fields have stood in the way of progress in integrating modeling into undergraduate courses.…”

“…Scaffolding competencies across course series or whole programs will require thoughtful reflection on the component parts of each learning outcome and how students develop these outcomes over time. To assist in this work, there are a number of resources focusing on particular competencies (for example, see Angra & Gardner, 2016;Diaz-Martinez et al, 2019;Diaz Eaton et al, 2019;Pelaez et al, 2017;Quillin & Thomas, 2015;Tripp & Shortlidge, 2019;Wilson Sayres et al, 2018), all of which describe specific competencies in further detail than is contained in the BioSkills Guide. Additionally, work in K-12 education, and more recently higher education, developing learning progressions could guide future investigations of competency scaffolding (Schwarz et al, 2009;Scott, Wenderoth, & Doherty, 2019).…”

“…Since then, two groups have unpacked the core concepts into more detailed frameworks Cary & Branchaw, 2017). For competencies, biology education researchers have enumerated a variety of specific scientific practices, including: science process skills (Coil, Wenderoth, Cunningham, & Dirks, 2010), experimentation (Pelaez et al, 2017), scientific literacy (Gormally, Brickman, & Lutz, 2012), responsible conduct of research (Diaz-Martinez et al, 2019), quantitative reasoning (Durán & Marshall, 2018;Stanhope et al, 2017), bioinformatics (Wilson Sayres et al, 2018), data science (Kjelvik & Schultheis, 2019), data communication (Angra & Gardner, 2016), modeling (Diaz Eaton et al, 2019;Quillin & Thomas, 2015), the interdisciplinary nature of science (Tripp & Shortlidge, 2019), and scientific writing (Timmerman, Strickland, Johnson, & Payne, 2011). Efforts to define general or STEM-wide education goals for college graduates can also inform how we teach competencies in biology, such as the Association of American College & University VALUE rubrics (Rhodes, 2010) and more targeted work on information literacy (Association of College and Research Libraries, 2015), communication (Mercer-Mapstone & Kuchel, 2017), and process skills (Cole, Lantz, Ruder, Reynders, & Stanford, 2018;Understanding Science, 2016).…”

Changes in students’ mental models from computational modeling of gene regulatory networks

“…Modeling (frequently coupled with simulations) is a skill that develops over time and is considered a core competency for postsecondary biology students (AAAS 2011). Modeling can involve a spectrum of skills from passive observation of graphs to creation and interpretation of models ranging from conceptual to computational (Eaton et al 2019;Garfunkel and Montgomery 2016). Moreover, scientists engage in modeling for many reasons, including exploring dynamics of complex systems, developing conceptual frameworks, making predictions, and generating causal explanations (Nersessian 2009;Odenbaugh 2005;Svoboda and Passmore 2013).…”