Company, events, and community
2021-01-21
Feedback at scale
Share
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.
Speakers
Mine Çetinkaya-Rundel
Data science educator at Duke University and Posit
Dr. Mine Çetinkaya-Rundel, a leading voice in data science education and a passionate advocate for making data and statistics more accessible, engaging, and reproducible. Mine is Professor of the Practice at Duke University in the Department of Statistical Science and Senior Developer Advocate at Posit, PBC, where she’s part of the tidyverse team.
Her work has transformed how data science is taught, emphasizing openness, reproducibility, and hands-on learning. She’s a co-author of influential open textbooks like R for Data Science and OpenIntro Statistics, which have guided countless learners on their journey into statistics and data science.
Mine’s contributions to education and open science have earned her several accolades, including the Waller Education Award by the American Statistical Association, the Robert V. Hogg Award by the Mathematical Association of America, and the Pickard Award by Harvard University's Department of Statistics, honoring her innovative teaching and dedication to expanding access to statistics and data science education.
Through her teaching, open-source work, and advocacy, Mine continues to inspire new generations of data scientists to not only analyze data—but to do so with curiosity, creativity, and purpose.
Her work has transformed how data science is taught, emphasizing openness, reproducibility, and hands-on learning. She’s a co-author of influential open textbooks like R for Data Science and OpenIntro Statistics, which have guided countless learners on their journey into statistics and data science.
Mine’s contributions to education and open science have earned her several accolades, including the Waller Education Award by the American Statistical Association, the Robert V. Hogg Award by the Mathematical Association of America, and the Pickard Award by Harvard University's Department of Statistics, honoring her innovative teaching and dedication to expanding access to statistics and data science education.
Through her teaching, open-source work, and advocacy, Mine continues to inspire new generations of data scientists to not only analyze data—but to do so with curiosity, creativity, and purpose.