GroverCode : code canonicalization and clustering applied to grading
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
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Format: | Thesis |
Language: | eng |
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Massachusetts Institute of Technology
2017
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Online Access: | http://hdl.handle.net/1721.1/106381 |
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author | Terman, Stacey (Stacey E.) |
author2 | Robert C. Miller. |
author_facet | Robert C. Miller. Terman, Stacey (Stacey E.) |
author_sort | Terman, Stacey (Stacey E.) |
collection | MIT |
description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. |
first_indexed | 2024-09-23T09:11:53Z |
format | Thesis |
id | mit-1721.1/106381 |
institution | Massachusetts Institute of Technology |
language | eng |
last_indexed | 2024-09-23T09:11:53Z |
publishDate | 2017 |
publisher | Massachusetts Institute of Technology |
record_format | dspace |
spelling | mit-1721.1/1063812019-04-12T09:03:26Z GroverCode : code canonicalization and clustering applied to grading Code canonicalization and clustering applied to grading Terman, Stacey (Stacey E.) Robert C. Miller. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (page 51). Teachers of MOOCs need to analyze large quantities of student submissions. There are a few systems designed to provide feedback at scale. Adapting these systems for residential courses would provide a substantial benefit for instructors, as a large residential course might still have several hundred students. OverCode, one such system, clusters and canonicalizes student submissions that have been marked correct by an autograder. We present GroverCode, an expanded version of OverCode that canonicalizes incorrect student submissions as well, and includes interface features for assigning grades to submissions. GroverCode was deployed in 6.0001, an introductory Python programming course, to assist teaching staff in grading exams. Overall reactions to the system were very positive. by Stacey Terman. M. Eng. 2017-01-12T18:18:29Z 2017-01-12T18:18:29Z 2016 2016 Thesis http://hdl.handle.net/1721.1/106381 967657961 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 51 pages application/pdf Massachusetts Institute of Technology |
spellingShingle | Electrical Engineering and Computer Science. Terman, Stacey (Stacey E.) GroverCode : code canonicalization and clustering applied to grading |
title | GroverCode : code canonicalization and clustering applied to grading |
title_full | GroverCode : code canonicalization and clustering applied to grading |
title_fullStr | GroverCode : code canonicalization and clustering applied to grading |
title_full_unstemmed | GroverCode : code canonicalization and clustering applied to grading |
title_short | GroverCode : code canonicalization and clustering applied to grading |
title_sort | grovercode code canonicalization and clustering applied to grading |
topic | Electrical Engineering and Computer Science. |
url | http://hdl.handle.net/1721.1/106381 |
work_keys_str_mv | AT termanstaceystaceye grovercodecodecanonicalizationandclusteringappliedtograding AT termanstaceystaceye codecanonicalizationandclusteringappliedtograding |