Leveraging Learners for Teaching Programming and Hardware Design at Scale
In a massive open online course (MOOC), a single pro-gramming or digital hardware design exercise may yield thousands of student solutions that vary in many ways, some superï¬ cial and some fundamental. Understanding large-scale variation in student solutions is a hard but important problem. For tea...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computing Machinery (ACM)
2017
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| Online Access: | http://hdl.handle.net/1721.1/112397 https://orcid.org/0000-0001-5178-3496 https://orcid.org/0000-0002-0442-691X |
| _version_ | 1826197197245382656 |
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| author | Glassman, Elena L Miller, Robert C |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Glassman, Elena L Miller, Robert C |
| author_sort | Glassman, Elena L |
| collection | MIT |
| description | In a massive open online course (MOOC), a single pro-gramming or digital hardware design exercise may yield thousands of student solutions that vary in many ways, some superï¬ cial and some fundamental. Understanding large-scale variation in student solutions is a hard but important problem. For teachers, this variation can be a source of pedagogically valuable examples and expose corner cases not yet covered by autograding. For students, the variation in a large class means that other students may have struggled along a similar solution path, hit the same bugs, and can offer hints based on that earned expertise. We developed three systems to take advantage of the solu-tion variation in large classes, using program analysis and learnersourcing. All three systems have been evaluated using data or live deployments in on-campus or edX courses with thousands of students. |
| first_indexed | 2024-09-23T10:44:00Z |
| format | Article |
| id | mit-1721.1/112397 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:44:00Z |
| publishDate | 2017 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1123972022-09-30T22:38:20Z Leveraging Learners for Teaching Programming and Hardware Design at Scale Glassman, Elena L Miller, Robert C Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Miller, Robert C. Glassman, Elena L Miller, Robert C In a massive open online course (MOOC), a single pro-gramming or digital hardware design exercise may yield thousands of student solutions that vary in many ways, some superï¬ cial and some fundamental. Understanding large-scale variation in student solutions is a hard but important problem. For teachers, this variation can be a source of pedagogically valuable examples and expose corner cases not yet covered by autograding. For students, the variation in a large class means that other students may have struggled along a similar solution path, hit the same bugs, and can offer hints based on that earned expertise. We developed three systems to take advantage of the solu-tion variation in large classes, using program analysis and learnersourcing. All three systems have been evaluated using data or live deployments in on-campus or edX courses with thousands of students. 2017-12-05T16:26:36Z 2017-12-05T16:26:36Z 2016-02 Article http://purl.org/eprint/type/ConferencePaper 978-1-4503-3950-6 http://hdl.handle.net/1721.1/112397 Glassman, Elena, and Miller, Robert. “Leveraging Learners for Teaching Programming and Hardware Design at Scale.” Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion (CSCW 2016) (February 2016): 37-40 © 2016 Association for Computing Machinery (ACM) https://orcid.org/0000-0001-5178-3496 https://orcid.org/0000-0002-0442-691X en_US http://dx.doi.org/10.1145/2818052.2874319 Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion (CSCW 2016) Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Association for Computing Machinery (ACM) Miller |
| spellingShingle | Glassman, Elena L Miller, Robert C Leveraging Learners for Teaching Programming and Hardware Design at Scale |
| title | Leveraging Learners for Teaching Programming and Hardware Design at Scale |
| title_full | Leveraging Learners for Teaching Programming and Hardware Design at Scale |
| title_fullStr | Leveraging Learners for Teaching Programming and Hardware Design at Scale |
| title_full_unstemmed | Leveraging Learners for Teaching Programming and Hardware Design at Scale |
| title_short | Leveraging Learners for Teaching Programming and Hardware Design at Scale |
| title_sort | leveraging learners for teaching programming and hardware design at scale |
| url | http://hdl.handle.net/1721.1/112397 https://orcid.org/0000-0001-5178-3496 https://orcid.org/0000-0002-0442-691X |
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