GroverCode : code canonicalization and clustering applied to grading

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Terman, Stacey (Stacey E.)

Advisor dc:contributor.advisor

• Robert C. Miller.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/106381

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/106381