Feature engineering for clustering student solutions
Open-ended homework problems such as coding assignments give students a broad range of freedom for the design of solutions. We aim to use the diversity in correct solutions to enhance student learning by automatically suggesting alternate solutions. Our approach is to perform a two-level hierarchica...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computing Machinery (ACM)
2014
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| Online Access: | http://hdl.handle.net/1721.1/90409 https://orcid.org/0000-0001-5178-3496 https://orcid.org/0000-0002-0442-691X |
| _version_ | 1826194039837294592 |
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| author | Glassman, Elena L. Singh, Rishabh 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. Singh, Rishabh Miller, Robert C. |
| author_sort | Glassman, Elena L. |
| collection | MIT |
| description | Open-ended homework problems such as coding assignments give students a broad range of freedom for the design of solutions. We aim to use the diversity in correct solutions to enhance student learning by automatically suggesting alternate solutions. Our approach is to perform a two-level hierarchical clustering of student solutions to first partition them based on the choice of algorithm and then partition solutions implementing the same algorithm based on low-level implementation details. Our initial investigations in domains of introductory programming and computer architecture demonstrate that we need two different classes of features to perform effective clustering at the two levels, namely abstract features and concrete features. |
| first_indexed | 2024-09-23T09:49:33Z |
| format | Article |
| id | mit-1721.1/90409 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:49:33Z |
| publishDate | 2014 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/904092022-09-30T17:03:07Z Feature engineering for clustering student solutions Glassman, Elena L. Singh, Rishabh Miller, Robert C. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Glassman, Elena L. Singh, Rishabh Miller, Robert C. Open-ended homework problems such as coding assignments give students a broad range of freedom for the design of solutions. We aim to use the diversity in correct solutions to enhance student learning by automatically suggesting alternate solutions. Our approach is to perform a two-level hierarchical clustering of student solutions to first partition them based on the choice of algorithm and then partition solutions implementing the same algorithm based on low-level implementation details. Our initial investigations in domains of introductory programming and computer architecture demonstrate that we need two different classes of features to perform effective clustering at the two levels, namely abstract features and concrete features. 2014-09-26T17:54:57Z 2014-09-26T17:54:57Z 2014-03 Article http://purl.org/eprint/type/ConferencePaper 9781450326698 http://hdl.handle.net/1721.1/90409 Elena L. Glassman, Rishabh Singh, and Robert C. Miller. 2014. Feature engineering for clustering student solutions. In Proceedings of the first ACM conference on Learning @ scale conference (L@S '14). ACM, New York, NY, USA, 171-172. https://orcid.org/0000-0001-5178-3496 https://orcid.org/0000-0002-0442-691X en_US http://dx.doi.org/10.1145/2556325.2567865 Proceedings of the first ACM conference on Learning @ scale conference (L@S '14) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) MIT web domain |
| spellingShingle | Glassman, Elena L. Singh, Rishabh Miller, Robert C. Feature engineering for clustering student solutions |
| title | Feature engineering for clustering student solutions |
| title_full | Feature engineering for clustering student solutions |
| title_fullStr | Feature engineering for clustering student solutions |
| title_full_unstemmed | Feature engineering for clustering student solutions |
| title_short | Feature engineering for clustering student solutions |
| title_sort | feature engineering for clustering student solutions |
| url | http://hdl.handle.net/1721.1/90409 https://orcid.org/0000-0001-5178-3496 https://orcid.org/0000-0002-0442-691X |
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