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

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.

FIGURE 1

Overall means (± 1 SD) for life science majors’ mathbiology task values (interest n = 1,015, utility value n = 994, cost n = 991).

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).

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).

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).

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).