The case for biocalculus: Improving student understanding of the utility value of mathematics to biology and affect toward mathematics

The next generation of life science professionals will require far more quantitative skills than prior generations. Calculus is important for understanding dynamical systems in biology and, therefore, is often a required course for life science students. However, many life science students do not understand the utility value of mathematics to biology. Therefore, according to expectancy-value theory, life science students may experience lower moti-vation, which can impact their performance in a calculus course. This study examines how two different biocalculus courses, which integrated calculus and biological concepts and successfully halved the rates of students earning a D, F, or withdrawing (DFW), affected life science students’ utility value, interest, and overall attitudes toward mathematics. Using pre and post surveys, we found that students’ interest in mathematics increased by the end of the semester, and they demonstrated a more sophisticated understanding of how mathematics is used in biology. Students whose attitudes toward mathematics improved primarily attributed these changes to a better understanding of the utility of mathematics to biology, feelings of competence in mathematics, or rapport with the instructor. Thus, communicating the utility value of mathematics to biology through integrated mathemat-ics–biology courses can contribute to improved attitudes toward mathematics that can impact students’ motivation and performance

Beyond linear regression: A reference for analyzing common data types in discipline based education research

[This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A common goal in discipline-based education research (DBER) is to determine how to improve student outcomes. Linear regression is a common technique used to test hypotheses about the effects of interventions on continuous outcomes (such as exam score) as well as control for student nonequivalence in quasirandom experimental designs. (In quasirandom designs, subjects are not randomly assigned to treatments. For example, when treatment is assigned by classroom, and observations are made on students, the design is quasirandom because treatment is assigned to classroom, not subject (students).) However, many types of outcome data cannot be appropriately analyzed with linear regression. In these instances, researchers must move beyond linear regression and implement alternative regression techniques. For example, student outcomes can be measured on binary scales (e.g., pass or fail), tightly bound scales (e.g., strongly agree to strongly disagree), or nominal scales (i.e., different discrete choices for example multiple tracks within a physics major), each necessitating alternative regression techniques. Here, we review extensions of linear modeling—generalized linear models (glms)—and specifically compare five glms that are useful for analyzing DBER data: logistic, binomial, proportional odds (also called ordinal; including censored regression), multinomial, and Poisson (including negative binomial, hurdle, and zero-inflated) regression. We introduce a diagnostic tool to facilitate a researcher’s identification of the most appropriate glm for their own data. For each model type, we explain when, why, and how to implement the regression approach. When: we provide examples of the types of research questions and outcome data that would motivate this regression approach, including citations to articles in the DBER literature. Why: we name which linear regression assumption is violated by the data type. How: we detail implementation and interpretation of this modeling approach in R, including R syntax and code, and how to discuss the regression output in research papers. Code accompanying each analysis can be found in the online github repository that is associated with this paper (https://github.com/ejtheobald/BeyondLinearRegression). This paper is not an exhaustive review of regression techniques, nor does it review nonregression-based analyses. Rather, it aims to compile and summarize regression techniques useful for the most common types of DBER data and provide examples, citations, and heavily annotated R code so that researchers can easily implement the technique in their work

A Social Capital Perspective on the Mentoring of Undergraduate Life Science Researchers: An Empirical Study of Undergraduate–Postgraduate–Faculty Triads

Undergraduate researchers at research universities are often mentored by graduate students or postdoctoral researchers (referred to collectively as “postgraduates”) and faculty, creating a mentoring triad structure. Triads differ based on whether the undergraduate, postgraduate, and faculty member interact with one another about the undergraduate’s research. Using a social capital theory framework, we hypothesized that different triad structures provide undergraduates with varying resources (e.g., information, advice, psychosocial support) from the postgraduates and/or faculty, which would affect the undergraduates’ research outcomes. To test this, we collected data from a national sample of undergraduate life science researchers about their mentoring triad structure and a range of outcomes associated with research experiences, such as perceived gains in their abilities to think and work like scientists, science identity, and intentions to enroll in a PhD program. Undergraduates mentored by postgraduates alone reported positive outcomes, indicating that postgraduates can be effective mentors. However, undergraduates who interacted directly with faculty realized greater outcomes, suggesting that faculty interaction is important for undergraduates to realize the full benefits of research. The “closed triad,” in which undergraduates, postgraduates, and faculty all interact directly, appeared to be uniquely beneficial; these undergraduates reported the highest gains in thinking and working like a scientist

A Guide for Graduate Students Interested in Postdoctoral Positions in Biology Education Research

Intended as a resource for life sciences graduate students, this essay discusses the diversity of postdoctoral positions in biology education and the careers to which they lead. The authors also provide advice to help graduate students develop the skills necessary to obtain a biology education research postdoctoral position

Community College Instructors’ Perceptions of Constraints and Affordances Related to Teaching Quantitative Biology Skills and Concepts

Quantitative skills are an important competency for undergraduate biology students and should be incorporated early and frequently in an undergraduate&rsquo;s career. Community colleges (CCs) are responsible for teaching introductory biology to a large proportion of biology and prehealth students, and quantitative skills are critical for their careers. However, we know little about the challenges and affordances that CC instructors encounter when incorporating quantitative skills into their courses. To explore this, we interviewed CC biology instructors ( n = 20) about incorporating quantitative biology (QB) instruction into their classes. We used a purposeful sampling approach to recruit instructors who were likely to have tried evidence-based pedagogies and were likely aware of the importance of QB instruction. We used open coding to identify themes related to the affordances to and constraints on teaching QB. Overall, our study participants met with challenges typical of incorporating new material or techniques into any college-level class, including perceptions of student deficits, tension between time to teach quantitative skills and cover biology content, and gaps in teacher professional knowledge (e.g., content and pedagogical content knowledge). We analyze these challenges and offer potential solutions and recommendations for professional development to support QB instruction at CCs.</p

Body size and digestive system shape resource selection by ungulates : a cross-taxa test of the forage maturation hypothesis

The forage maturation hypothesis (FMH) states that energy intake for ungulates is maximised when forage biomass is at intermediate levels. Nevertheless, metabolic allometry and different digestive systems suggest that resource selection should vary across ungulate species. By combining GPS relocations with remotely sensed data on forage characteristics and surface water, we quantified the effect of body size and digestive system in determining movements of 30 populations of hindgut fermenters (equids) and ruminants across biomes. Selection for intermediate forage biomass was negatively related to body size, regardless of digestive system. Selection for proximity to surface water was stronger for equids relative to ruminants, regardless of body size. To be more generalisable, we suggest that the FMH explicitly incorporate contingencies in body size and digestive system, with small-bodied ruminants selecting more strongly for potential energy intake, and hindgut fermenters selecting more strongly for surface water.DATA AVAILABILITY STATEMENT : The dataset used in our analyses is available via Dryad repository (https://doi.org/10.5061/dryad.jsxksn09f) following a year-long embargo from publication of the manuscript. The coordinates associated with mountain zebra data are not provided in an effort to protect critically endangered black rhino (Diceros bicornis) locations. Interested researchers can contact the data owner (Minnesota Zoo) directly for inquiries.https://wileyonlinelibrary.com/journal/elehj2022Mammal Research InstituteZoology and Entomolog

Appendix B. Detailed description of the methods and two tables and one figure presenting results of the parameterization of vital-rate functions for the integral projection models (IPM).

Detailed description of the methods and two tables and one figure presenting results of the parameterization of vital-rate functions for the integral projection models (IPM)

Appendix A. Three tables presenting detailed description of the study sites.

Three tables presenting detailed description of the study sites

Deidentified data used to develop the Math-Biology Values Instrument

This dataset contains the deidentified data used in the validation process for the Math-Biology Values Instrument (MBVI). MBVI_spring2016 contains data collected from undergraduate life science majors (via electronic survey) to develop the MBVI and was used for exploratory factor analyses and establishing convergent and divergent validity. MBVI_fall2016 contains data collected from a second independent sample of undergraduate life science majors (also via electronic survey) that was used for confirmatory factor analyses. The two "key" files contain survey item text, response options, and notes for all column headings in the data files. A full description of the data collection process and analyses can be found in the related publication cited below