Development of a Biological Science Quantitative Reasoning Exam (BioSQuaRE)

Abstract

Recent national reports call for increasing the quantitative acumen of biology students. The BioSQuaRE represents an assessment tool based on such reports. The iterative development of the instrument by science and mathematics faculty in collaboration with educational psychologists is described, and the tool’s psychometric properties are summarized.

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).…”

How strongly does statistical reasoning influence knowledge and acceptance of evolution?

“…Research in biology education has begun to examine the roles that mathematical and statistical reasoning might play in learning about biological concepts (e.g., Chiel, McManus, & Shaw, ; Hester, Buxner, Elfring, & Nagy, ; Metz, ; Stanhope et al, ; Wachsmuth, Runyon, Drake, & Dolan, ). However, to our knowledge, the field lacks large‐scale empirical research examining quantitative relationships among statistical reasoning, evolution understanding, and evolution acceptance (cf.…”

The Conceptualization of Quantitative Reasoning among Introductory Biology Faculty

“…For each interview, statements in response to questions regarding conceptualization of QR were coded (see below); where appropriate, an individual statement would be assigned multiple codes. The data analysis was informed by the interview questions, QR definitions in educational research (e.g., references 40 – 42 ), and the Vision and Change ( 3 ) document. These artifacts helped deductively frame the initial and overarching codes such as “Meaning of QR” and “Faculty Background in QR.” As suggested by the phenomenological data analysis approach, we utilized “horizontalization” highlighting significant statements across interviews to help us understand the essence of the teaching QR experience.…”