The Math–Biology Values Instrument: Development of a Tool to Measure Life Science Majors’ Task Values of Using Math in the Context of Biology

Andrews, Sarah; Runyon, Christopher; Aikens, Melissa L.

doi:10.1187/cbe.17-03-0043

Cited by 28 publications

(49 citation statements)

References 77 publications

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“…Longstanding awareness of the centrality of mathematical and statistical competencies for biology education is reflected in numerous policy reports, editorials, and position statements (AAAS, ; NRC, ). Discussions of the importance of these competencies have not been matched by action; undergraduate life science education in the United States (and other nations) has been slow to integrate quantitative literacy into the curriculum (AAAS, ; Andrews, Runyon, & Aikens, ). Notable recent attempts, however, include the Supporting Faculty in Quantitative Undergraduate Biology Education and Synthesis (QUBES) project, a million‐dollar project funded by the U.S. National Science Foundation (Donovan et al, ).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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A large body of research has examined students' conceptions of evolution and their relationships to acceptance of evolution. Proficiency in statistical and probabilistic reasoning has long been considered to be an essential feature of evolutionary reasoning, yet almost no empirical work has explored these putative connections. The RaPro instruments have recently been developed to measure statistical reasoning in the contexts of mathematics (RaProMath) and evolution (RaProEvo). Our study provides additional validation of these instruments using Rasch analysis and quantifies the contribution of statistical reasoning to both understanding and accepting evolution. We recruited a large sample (N = 564) of undergraduate students enrolled in an introductory biology course at a large public research university in the United States. Students completed a suite of published instruments that assessed statistical reasoning, evolutionary understanding, and evolutionary acceptance. Our findings indicate that validity inferences derived from RaPro scores generalized to the new sample, and that proficiency in statistical reasoning explained 28% of the variance in evolutionary knowledge and 19% of the variation in evolutionary acceptance. The inclusion of demographic variables into the model significantly increased the explained variance in acceptance. Notably, the variance in evolution acceptance explained by statistical reasoning was comparable to that of thinking dispositions or evolutionary knowledge reported in the literature. This work provides the first large-scale evidence of the role of statistical reasoning in evolutionary knowledge and acceptance and motivates future work to explore how statistical literacy should be integrated into evolution education efforts.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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“…Finally, we recommend that further research on undergraduate science and math students’ emotional satisfaction with math be coupled with research in other attitudinal domains such as life science students’ value of math. Andrews and colleagues have recently developed the Math-Biology Values Instrument (MBVI) for measuring life science majors’ math values from the perspective of expectancy value theory ( Wigfield and Eccles, 1992 , 2000 ; Andrews et al. , 2017 ).…”

Section: Discussionmentioning

confidence: 99%

“…Students who report neutral or lower levels of emotional satisfaction may need mathematical work to be framed in terms of its personal relevance or utility to find the work satisfying ( Walkington et al. , 2013 ; Walkington and Bernacki, 2014 ), an idea that could be tested using the MBVI ( Andrews et al. , 2017 ) and emotional satisfaction scale in concert.…”

Section: Discussionmentioning

confidence: 99%

Do Biology Students Really Hate Math? Empirical Insights into Undergraduate Life Science Majors’ Emotions about Mathematics

Wachsmuth

Runyon

Drake

et al. 2017

LSE

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“…The online survey contained the Math-Biology Values Instrument (MBVI) ( 30 ), two items on students’ likelihood of choosing to take advanced quantitative biology courses, and a demographic/academic questionnaire. The MBVI was developed and validated as a measure of undergraduate life science majors’ math-biology task values (interest, utility value, and cost) and contained 11 Likert-type items on a 7-point response scale ranging from “strongly disagree” to “strongly agree” ( 30 ) ( Appendix 1 ). Each subscale showed high internal consistency (interest: α = 0.96, n = 1,015; utility value: α = 0.90, n = 994; cost: α = 0.85, n = 991).…”

Section: Methodsmentioning

confidence: 99%

“…For example, Thompson and colleagues ( 5 ) found that instructors believed biology students had negative attitudes about mathematics in their biology courses; however, when students were polled, their attitudes were much more positive than predicted by instructors. Others found that students often believe mathematics is useful for their life science career ( 26 – 30 ). In contrast, recent empirical work found STEM majors reported mathematics to be slightly uncomfortable, frustrating, and unpleasant ( 31 ).…”

Section: Introductionmentioning

confidence: 99%

Life Science Majors’ Math-Biology Task Values Relate to Student Characteristics and Predict the Likelihood of Taking Quantitative Biology Courses

Andrews

Aikens

2018

J Microbiol Biol Educ.

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Expectancy-value theory of achievement motivation predicts that students’ task values, which include their interest in and enjoyment of a task, their perceptions of the usefulness of a task (utility value), and their perceptions of the costs of engaging in the task (e.g., extra effort, anxiety), influence their achievement and academic-related choices. Further, these task values are theorized to be informed by students’ sociocultural background. Although biology students are often considered to be math-averse, there is little empirical evidence of students’ values of mathematics in the context of biology (math-biology task values). To fill this gap in knowledge, we sought to determine 1) life science majors’ math-biology task values, 2) how math-biology task values differ according to students’ sociocultural background, and 3) whether math-biology task values predict students’ likelihood of taking quantitative biology courses. We surveyed life science majors about their likelihood of choosing to take quantitative biology courses and their interest in using mathematics to understand biology, the utility value of mathematics for their life science career, and the cost of doing mathematics in biology courses. Students on average reported some cost associated with doing mathematics in biology; however, they also reported high utility value and were more interested in using mathematics to understand biology than previously believed. Women and first-generation students reported more negative math-biology task values than men and continuing-generation students. Finally, students’ math-biology task values predicted their likelihood of taking biomodeling and biostatistics courses. Instructional strategies promoting positive math-biology task values could be particularly beneficial for women and first-generation students, increasing the likelihood that students would choose to take advanced quantitative biology courses.

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The Math–Biology Values Instrument: Development of a Tool to Measure Life Science Majors’ Task Values of Using Math in the Context of Biology

Cited by 28 publications

References 77 publications

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

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

Do Biology Students Really Hate Math? Empirical Insights into Undergraduate Life Science Majors’ Emotions about Mathematics

Life Science Majors’ Math-Biology Task Values Relate to Student Characteristics and Predict the Likelihood of Taking Quantitative Biology Courses

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