Maintaining Academic Integrity in Programming: Locality-Sensitive Hashing and Recommendations
Not many efficient similarity detectors are employed in practice to maintain academic integrity. Perhaps it is because they lack intuitive reports for investigation, they only have a command line interface, and/or they are not publicly accessible. This paper presents SSTRANGE, an efficient similarit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Education Sciences |
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| Online Access: | https://www.mdpi.com/2227-7102/13/1/54 |