In this paper, we present alternative ways to incorporate into biology curricula the application of mathematical tools, specifically statistics, that help students see the advantage of developing quantitative skills in order to analyze different kinds of biological data and discover the patterns and processes occurring in that data.
A recurrent theme throughout the resulting recommendations and guidelines is that undergraduate biology students must develop quantitative skills and learn to apply them in biological contexts (National Research Council [NRC], 2003, 2009; Association of American Medical Colleges and Howard Hughes Medical Institute [AAMC/HHMI], 2009; Labov et al., 2010; Woodin et al., 2010).
The biologist should have "highly developed quantitative skills" and be "well versed to facilitate dialogue with other researchers".
Nor do we mean to say that developing quantitative skills and reading primary literature are not important tasks for undergraduates.
Students require a sense of relevance, extensive practice, and prompt feedback in order to develop confidence and proficiency applying quantitative skills in biological contexts.
Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology.
In fact, students themselves report the need for providing more opportunities outside of the classroom and more opportunities to develop the quantitative skills necessary to be successful in research (NRC, 2009).
Students may need assistance to integrate their mathematical knowledge into their biology courses or even to develop the basic quantitative skills essential for success in updated introductory biology courses.
The ASSISTments research team at WPI took the lead on developing these quantitative skill building modules.
The National Science Education Standards and education research literature emphasize that students need to develop skills in quantitative data analysis (NRC, 2003; Bialek and Botstein, 2004; AAAS, 2011; Feser et al., 2013).
