Peer and Self Assessment in Massive Online Classes

Chinmay Kulkarni (), Koh Pang Wei (), Huy Le (), Daniel Chia (), Kathryn Papadopoulos (), Justin Cheng (), Daphne Koller () and Scott R. Klemmer ()
Additional contact information
Chinmay Kulkarni: Stanford University
Koh Pang Wei: Stanford University
Huy Le: Coursera, Inc.
Daniel Chia: Stanford University
Kathryn Papadopoulos: Stanford University
Justin Cheng: Stanford University
Daphne Koller: Stanford University
Scott R. Klemmer: Stanford University

A chapter in Design Thinking Research, 2015, pp 131-168 from Springer

Abstract: Abstract Peer and self assessment offer an opportunity to scale both assessment and learning to global classrooms. This paper reports our experiences with two iterations of the first large online class to use peer and self assessment. In this class, peer grades correlated highly with staff-assigned grades. The second iteration had 42.9 % of students’ grades within 5 % of the staff grade, and 65.5 % within 10 %. On average, students assessed their work 7 % higher than staff did. Students also rated peers’ work from their own country 3.6 % higher than those from elsewhere. We performed three experiments to improve grading accuracy. We found that giving students feedback about their grading bias increased subsequent accuracy. We introduce short, customizable feedback snippets that cover common issues with assignments, providing students more qualitative peer feedback. Finally, we introduce a data-driven approach that highlights high-variance items for improvement. We find that rubrics that use a parallel sentence structure, unambiguous wording and well-specified dimensions have lower variance. After revising rubrics, median grading error decreased from 12.4 to 9.9 %.

Keywords: Median Grade; Personalized Feedback; Qualitative Feedback; Design Studio; Peer Assessment (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-319-06823-7_9

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