{"title":"一个使用人工智能和上下文感知技术自动检测在线作弊活动的智能系统","authors":"Qinyuhan Zhao, Mingze Gao, Yu Sun","doi":"10.5121/csit.2022.121707","DOIUrl":null,"url":null,"abstract":"In the environment of online courses and online exams, cheating in online courses is prevalent [1]. To better ensure fairness in exams, schools and educational institutions need to use technology to detect and deter cheating [2]. Starting from practical application, this paper discusses 3 different methods to detect cheating behavior, and proposes a new way. for online exam supervision.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Intelligent System to Automate the Detection of Online Cheating Activities using AI and Context Aware Techniques\",\"authors\":\"Qinyuhan Zhao, Mingze Gao, Yu Sun\",\"doi\":\"10.5121/csit.2022.121707\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the environment of online courses and online exams, cheating in online courses is prevalent [1]. To better ensure fairness in exams, schools and educational institutions need to use technology to detect and deter cheating [2]. Starting from practical application, this paper discusses 3 different methods to detect cheating behavior, and proposes a new way. for online exam supervision.\",\"PeriodicalId\":170432,\"journal\":{\"name\":\"Signal & Image Processing Trends\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Signal & Image Processing Trends\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/csit.2022.121707\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal & Image Processing Trends","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/csit.2022.121707","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Intelligent System to Automate the Detection of Online Cheating Activities using AI and Context Aware Techniques
In the environment of online courses and online exams, cheating in online courses is prevalent [1]. To better ensure fairness in exams, schools and educational institutions need to use technology to detect and deter cheating [2]. Starting from practical application, this paper discusses 3 different methods to detect cheating behavior, and proposes a new way. for online exam supervision.
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