Life Science Majors’ Math-Biology Task Values Relate to Student Characteristics and Predict the Likelihood of Taking Quantitative Biology Courses †
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Authors:
Sarah E. Andrews1,*,
Melissa L. Aikens1
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Received 07 February 2018 Accepted 04 June 2018 Published 31 July 2018
- ©2018 Author(s). Published by the American Society for Microbiology.
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[open-access] This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial-NoDerivatives 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/ and https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode), which grants the public the nonexclusive right to copy, distribute, or display the published work.
- *Corresponding author. Mailing address: Department of Biological Sciences, 117 Spaulding Hall, 46 College Road, University of New Hampshire, Durham, NH 03824. Phone: 603-862-2379. E-mail: [email protected].
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1: Department of Biological Sciences, University of New Hampshire, Durham, NH 03824
AUTHOR AND ARTICLE INFORMATION
AUTHOR AND ARTICLE INFORMATION
Source:
J. Microbiol. Biol. Educ.
July 2018
vol. 19 no. 2 doi:10.1128/jmbe.v19i2.1589
MyBook is a cheap paperback edition of the original book and will be sold at uniform, low price.
Abstract:
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.
References & Citations
Supplemental Material
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Appendix 1: Additional methodology and details on the survey (instructions, items, and response options), Appendix 2: Correlations among all variables used in the study, Appendix 3: Unstandardized regression coefficients, standard error, and p values for fixed effects used in interest, utility value, and cost linear mixed-effects models, Appendix 4: Unstandardized regression coefficients, standard error, and p values for fixed effects used in likelihood of taking a biomodeling and biostatistics course linear mixed-effects models
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2018-07-31
2019-10-16
Abstract:
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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Author and Article Information
-
Received 07 February 2018 Accepted 04 June 2018 Published 31 July 2018
- ©2018 Author(s). Published by the American Society for Microbiology.
-
[open-access] This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial-NoDerivatives 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/ and https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode), which grants the public the nonexclusive right to copy, distribute, or display the published work.
- *Corresponding author. Mailing address: Department of Biological Sciences, 117 Spaulding Hall, 46 College Road, University of New Hampshire, Durham, NH 03824. Phone: 603-862-2379. E-mail: [email protected].
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Life Science Majors’ Math-Biology Task Values Relate to Student Characteristics and Predict the Likelihood of Taking Quantitative Biology Courses
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Citation:
Andrews S, Aikens M. 2018. Life science majors’ math-biology task values relate to student characteristics and predict the likelihood of taking quantitative biology courses
†
. J. Microbiol. Biol. Educ. 19(2): doi:10.1128/jmbe.v19i2.1589
/content/jmbe/10.1128/jmbe.v19i2.1589.f1-jmbe-19-80
FIGURE 1
Overall means (± 1 SD) for life science majors’ mathbiology task values (interest n = 1,015, utility value n = 994, cost n = 991).
jmbe/19/2/jmbe-19-80f1_thmb.gif
jmbe/19/2/jmbe-19-80f1.gif
Click to view
FIGURE 1
Overall means (± 1 SD) for life science majors’ mathbiology task values (interest n = 1,015, utility value n = 994, cost n = 991).
Source:
J. Microbiol. Biol. Educ.
July 2018
vol. 19 no. 2 doi:10.1128/jmbe.v19i2.1589
/content/jmbe/10.1128/jmbe.v19i2.1589.f2-jmbe-19-80
FIGURE 2
Effect of (A) gender and (B) first-generation status on life science majors’ math-biology interest (n = 1,015), utility value (n = 994), and cost (n = 991). Bars are marginal means (averaged over all other effects in each model) with 95% confidence intervals (*** p < 0.001, ** p < 0.01, * p < 0.05, ns = not significant; p values based on modeled regression coefficients).
jmbe/19/2/jmbe-19-80f2_thmb.gif
jmbe/19/2/jmbe-19-80f2.gif
Click to view
FIGURE 2
Effect of (A) gender and (B) first-generation status on life science majors’ math-biology interest (n = 1,015), utility value (n = 994), and cost (n = 991). Bars are marginal means (averaged over all other effects in each model) with 95% confidence intervals (*** p < 0.001, ** p < 0.01, * p < 0.05, ns = not significant; p values based on modeled regression coefficients).
Source:
J. Microbiol. Biol. Educ.
July 2018
vol. 19 no. 2 doi:10.1128/jmbe.v19i2.1589
/content/jmbe/10.1128/jmbe.v19i2.1589.f3-jmbe-19-80
FIGURE 3
Effect of race/ethnicity on life science majors’ mathbiology cost (n = 991). Bars are marginal means (averaged over all other effects in each model) with 95% confidence intervals. Indicators of significance are in comparison with the reference level, White (*** p < 0.001, ** p < 0.01, * p < 0.05, ns = not significant). Here we show uncorrected p values based on modeled regression coefficients; however, post-hoc comparisons using Tukey’s correction showed that none of the pairwise comparisons were significant (
Appendix 3
).
jmbe/19/2/jmbe-19-80f3_thmb.gif
jmbe/19/2/jmbe-19-80f3.gif
Click to view
FIGURE 3
Effect of race/ethnicity on life science majors’ mathbiology cost (n = 991). Bars are marginal means (averaged over all other effects in each model) with 95% confidence intervals. Indicators of significance are in comparison with the reference level, White (*** p < 0.001, ** p < 0.01, * p < 0.05, ns = not significant). Here we show uncorrected p values based on modeled regression coefficients; however, post-hoc comparisons using Tukey’s correction showed that none of the pairwise comparisons were significant ( Appendix 3 ).
Source:
J. Microbiol. Biol. Educ.
July 2018
vol. 19 no. 2 doi:10.1128/jmbe.v19i2.1589
/content/jmbe/10.1128/jmbe.v19i2.1589.f4-jmbe-19-80
FIGURE 4
Effect of highest high school mathematics course taken on life science majors’ math-biology interest (n = 1,015, p < 0.001), utility value (n = 994, p = 0.008), and cost (n = 991, p < 0.001). Bars are marginal means (averaged over all other effects in each model) with 95% confidence intervals. Within each task value, bars with different letters are significantly different (post-hoc pairwise comparisons were conducted using Tukey’s method for p value correction).
jmbe/19/2/jmbe-19-80f4_thmb.gif
jmbe/19/2/jmbe-19-80f4.gif
Click to view
FIGURE 4
Effect of highest high school mathematics course taken on life science majors’ math-biology interest (n = 1,015, p < 0.001), utility value (n = 994, p = 0.008), and cost (n = 991, p < 0.001). Bars are marginal means (averaged over all other effects in each model) with 95% confidence intervals. Within each task value, bars with different letters are significantly different (post-hoc pairwise comparisons were conducted using Tukey’s method for p value correction).
Source:
J. Microbiol. Biol. Educ.
July 2018
vol. 19 no. 2 doi:10.1128/jmbe.v19i2.1589
/content/jmbe/10.1128/jmbe.v19i2.1589.f5-jmbe-19-80
FIGURE 5
Predicting students’ likelihood of taking a biomodeling course (A–C; n = 954) or biostatistics course (D–F; n = 955) from their math-biology interest (left), utility value (center), or cost (right) scores. Note that “likelihood” is based on students’ responses to a 7-point Likert-type question (responses ranged from “Not at all likely” to “Very likely”). Regression lines are model predicted with 95% confidence intervals (shaded area).
jmbe/19/2/jmbe-19-80f5_thmb.gif
jmbe/19/2/jmbe-19-80f5.gif
Click to view
FIGURE 5
Predicting students’ likelihood of taking a biomodeling course (A–C; n = 954) or biostatistics course (D–F; n = 955) from their math-biology interest (left), utility value (center), or cost (right) scores. Note that “likelihood” is based on students’ responses to a 7-point Likert-type question (responses ranged from “Not at all likely” to “Very likely”). Regression lines are model predicted with 95% confidence intervals (shaded area).
Source:
J. Microbiol. Biol. Educ.
July 2018
vol. 19 no. 2 doi:10.1128/jmbe.v19i2.1589
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