2026-09-19T08:07:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1063812026-06-06T00:49:00Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Robert C. Miller.
Terman, Stacey (Stacey E.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2017-01-12T18:18:29Z
2017-01-12T18:18:29Z
2016
2016
http://hdl.handle.net/1721.1/106381
967657961
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.
51 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
GroverCode : code canonicalization and clustering applied to grading
Code canonicalization and clustering applied to grading
Thesis
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Publication
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<Abstract>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.</Abstract>
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