As enrollments in statistics and data science courses grow and as these courses become more computational, educators are faced with an interesting challenge — providing timely and meaningful feedback, particularly with online delivery of courses. The simplest solution is using assignments that are easier to auto-grade, e.g. multiple-choice questions, simplistic coding exercises, but it is impossible to assess mastery of the entire data science cycle using only these types of exercises. In this talk I will discuss writing effective learnr exercises, providing useful and motivating feedback with gradethis, distributing them at scale online and as an R package, and collecting student data for formative assessment with learnrhash.
Mine Çetinkaya-Rundel is Professor of the Practice in the Department of Statistical Science at Duke University and Developer Educator at Posit. Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education. She is a co-author of R for Data Science, Introduction to Modern Statistics, and OpenIntro Statistics. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Data Analysis with R and Data Science with R specializations on Coursera.
