Clegg, B., North, S. orcid.org/0000-0002-8478-8960, McMinn, P. orcid.org/0000-0001-9137-7433
et al. (1 more author)
(2019)
Simulating student mistakes to evaluate the fairness of automated grading.
In:
2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET).
41st International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), 25-31 May 2019, Montreal, QC, Canada.
IEEE
, pp. 121-125.
ISBN 9781728110011
Abstract
The use of autograding to assess programming students may lead to unfairness if an autograder is incorrectly configured. Mutation analysis offers a potential solution to this problem. By simulating student coding mistakes, an automated technique can evaluate the fairness and completeness of an autograding configuration. In this paper, we introduce a set of mutation operators to be used in such a technique, derived from a mistake classification of real student solutions for two introductory programming tasks.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | automated grading; mutation analysis; programming mistakes |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Jul 2020 13:44 |
Last Modified: | 15 Aug 2020 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICSE-SEET.2019.00021 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163399 |