{"title":"用击键动力学检测在线编程课中的合同作弊者","authors":"Jeongmin Byun, Jungkook Park, Alice H. Oh","doi":"10.1145/3386527.3406726","DOIUrl":null,"url":null,"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.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"409 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Detecting Contract Cheaters in Online Programming Classes with Keystroke Dynamics\",\"authors\":\"Jeongmin Byun, Jungkook Park, Alice H. Oh\",\"doi\":\"10.1145/3386527.3406726\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":20608,\"journal\":{\"name\":\"Proceedings of the Seventh ACM Conference on Learning @ Scale\",\"volume\":\"409 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Seventh ACM Conference on Learning @ Scale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3386527.3406726\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Seventh ACM Conference on Learning @ Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3386527.3406726","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detecting Contract Cheaters in Online Programming Classes with Keystroke Dynamics
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.