Associate Professor Pace University NEW YORK, New York, United States
Background/Question/Methods
New technologies have advanced our abilities to obtain large quantities of data in short time periods; yet personnel training has not kept pace with the acquisition of data. Data science skills should be taught early and often to undergraduate students, beginning as early as first year/freshman science courses. I designed a series of three modules for introductory organismal biology laboratories that teach undergraduate science majors fundamental research skills, beginning from formulating hypotheses, including data collection, and culminating in the use of R programming to construct confidence intervals to test hypotheses. The modules test for dimorphism in crayfish chelae, allometry in tapeworm hook structures, and differences in stomata densities among plant groups. The complexity of the analytical design of the experiments increases with each successive module. I use student course evaluations and a Statistical Reasoning in Biology Concept Inventory (Deane et al. 2016) to assess the effectiveness of these modules for student learning, enthusiasm, and achievement.
Results/Conclusions
The modules have been used and modified at Pace University (New York City) since 2013 in a large introductory course with laboratory sections of up to 20 students each. R scripts and template data collection files are available on GitHub. Additional course materials, including handouts and student worksheets, are available upon request. Students mostly commented on the delivery of the assessments for the course, which consist of weekly worksheets that each student submits after collaborating with classmates: “Labs and worksheets were completed in class with the help of lab partners and additional help from the professor. This allowed us to have a better understanding and complete knowledge of the questions and their answers, as opposed to guessing and potentially being wrong.”; “The handouts and worksheets Dr Crispo provided was not only helpful in class, but also proved as a great study source.”. Additional positive comments were made on the use of R programming for data analysis: “The R program really helped me understand the material.”; “I liked . . . being given the opportunity to learn R–Studio.” Results from a quantitative assessment of student learning gains in statistical reasoning will be presented.
