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 at Duke University and Developer Educator at RStudio. 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 as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. Mine works on the OpenIntro project, whose mission is to make educational products that are free, transparent, and lower barriers to education. As part of this project she co-authored four open-source introductory statistics textbooks. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera. Mine is a Fellow of the ASA and Elected Member of the ISI as well as the winner of the 2021 Robert V. Hogg Award for For Excellence in Teaching Introductory Statistics.
