Learnersourcing Personalized Hints
Name
Miller_Learnersourcing.pdf
Size
776.17 KB
Format
Adobe PDF
Checksum (MD5)
ee69df9a11b1838fa96b74bb7d3a1e32
Author(s)
Glassman, Elena L
Lin, Aaron S.
Cai, Carrie Jun
Miller, Robert C
Date Issued
February 2016
Journal
Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW '16
Publisher
Association for Computing Machinery
Citation
Glassman, Elena L., et al. "Learnersourcing Personalized Hints." Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, February 27 - March 02, 2016, San Francisco, California, ACM Press, 2016, pp. 1624–34.
Version
Author's final manuscript
Abstract
Personalized support for students is a gold standard in education, but it scales poorly with the number of students. Prior work on learnersourcing presented an approach for learners to engage in human computation tasks while trying to learn a new skill. Our key insight is that students, through their own experience struggling with a particular problem, can become experts on the particular optimizations they implement or bugs they resolve. These students can then generate hints for fellow students based on their new expertise. We present workflows that harvest and organize studentsâ collective knowledge and advice for helping fellow novices through design problems in engineering. Systems embodying each workflow were evaluated in the context of a college-level computer architecture class with an enrollment of more than two hundred students each semester. We show that, given our design choices, students can create helpful hints for their peers that augment or even replace teachersâ personalized assistance, when that assistance is not available.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2818048.2820011
