Learnersourcing Personalized Hints
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 thei...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery
2017
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| Online Access: | http://hdl.handle.net/1721.1/112927 https://orcid.org/0000-0001-5178-3496 https://orcid.org/0000-0001-9421-7128 https://orcid.org/0000-0002-0442-691X |
| _version_ | 1826199911690928128 |
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| author | Glassman, Elena L Lin, Aaron S. Cai, Carrie Jun Miller, Robert C |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Glassman, Elena L Lin, Aaron S. Cai, Carrie Jun Miller, Robert C |
| author_sort | Glassman, Elena L |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:28:06Z |
| format | Article |
| id | mit-1721.1/112927 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:28:06Z |
| publishDate | 2017 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | mit-1721.1/1129272022-10-01T03:49:54Z Learnersourcing Personalized Hints Glassman, Elena L Lin, Aaron S. Cai, Carrie Jun Miller, Robert C Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Glassman, Elena L Lin, Aaron S. Cai, Carrie Jun Miller, Robert C 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. 2017-12-21T20:22:13Z 2017-12-21T20:22:13Z 2016-02 Article http://purl.org/eprint/type/ConferencePaper 978-1-4503-3592-8 http://hdl.handle.net/1721.1/112927 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. https://orcid.org/0000-0001-5178-3496 https://orcid.org/0000-0001-9421-7128 https://orcid.org/0000-0002-0442-691X en_US http://dx.doi.org/10.1145/2818048.2820011 Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW '16 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery MIT Web Domain |
| spellingShingle | Glassman, Elena L Lin, Aaron S. Cai, Carrie Jun Miller, Robert C Learnersourcing Personalized Hints |
| title | Learnersourcing Personalized Hints |
| title_full | Learnersourcing Personalized Hints |
| title_fullStr | Learnersourcing Personalized Hints |
| title_full_unstemmed | Learnersourcing Personalized Hints |
| title_short | Learnersourcing Personalized Hints |
| title_sort | learnersourcing personalized hints |
| url | http://hdl.handle.net/1721.1/112927 https://orcid.org/0000-0001-5178-3496 https://orcid.org/0000-0001-9421-7128 https://orcid.org/0000-0002-0442-691X |
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