Teaching Reproducibility to First Year College Students: Reflections from an Introductory Data Science Course

Modern technology threatens traditional modes of classroom assessment by providing students with automated ways to write essays and take exams. At the same time, modern technology continues to expand the accessibility of computational tools that promise to increase the potential scope and quality of class projects. This paper presents a case study where students are asked to complete a "reproducible" final project in an introductory data science course using the R programming language. A reproducible project is one where an instructor can easily regenerate the results and conclusions from the submitted materials. Experiences in two small sections of this introductory class suggest that reproducible projects are feasible to implement with only a little increase in assessment difficulty. The sample assignment presented in this paper, along with some proposed adaptations for non-data science classes, provide a pattern for directly assessing a student's analysis, rather than just the final results.