DC Field | Value | Language |
---|---|---|
dc.contributor.author | Byun, Jeongmin | ko |
dc.contributor.author | Park, Jungkook | ko |
dc.contributor.author | Oh, Alice | ko |
dc.date.accessioned | 2020-11-11T05:55:29Z | - |
dc.date.available | 2020-11-11T05:55:29Z | - |
dc.date.created | 2020-11-09 | - |
dc.date.created | 2020-11-09 | - |
dc.date.issued | 2020-08-14 | - |
dc.identifier.citation | 7th Annual ACM Conference on Learning at Scale, L@S 2020, pp.273 - 276 | - |
dc.identifier.uri | http://hdl.handle.net/10203/277217 | - |
dc.description.abstract | In online programming classes, it is tricky to uphold academic honesty in the assessment process. A common approach, plagiarism detection, is not accurate for novice programmers and ineffective for detecting contract cheaters. We present a new approach, cheating detection with keystroke dynamics in programming classes, and evaluated the approach. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Detecting Contract Cheaters in Online Programming Classes with Keystroke Dynamics | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85094925054 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 273 | - |
dc.citation.endingpage | 276 | - |
dc.citation.publicationname | 7th Annual ACM Conference on Learning at Scale, L@S 2020 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1145/3386527.3406726 | - |
dc.contributor.localauthor | Oh, Alice | - |
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