Anish Khazane, Jia Mao, India Irish, Rocko Graziano, Thad Starner
{"title":"带","authors":"Anish Khazane, Jia Mao, India Irish, Rocko Graziano, Thad Starner","doi":"10.1145/3386527.3406727","DOIUrl":null,"url":null,"abstract":"As online educational programs scale, monitoring peer collaboration in platforms like BlueJeans for plagiarism becomes difficult. Recent studies indicate that students are less likely to cheat if presented with direct warning messages prior to engaging in online activities. In this work, we present Bluejeans codE Leak deTection (BELT), a system that monitors online BlueJeans meetings for shared code and sends timely warning messages to meeting participants. To test BELT's robustness as an online proctor, we evaluate its code-text disambiguation, code detection from images of varying quality, and code detection from videos of varying resolution. We conclude this work by pinpointing areas of improvement and briefly discuss possible extensions for future work.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"BELT\",\"authors\":\"Anish Khazane, Jia Mao, India Irish, Rocko Graziano, Thad Starner\",\"doi\":\"10.1145/3386527.3406727\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As online educational programs scale, monitoring peer collaboration in platforms like BlueJeans for plagiarism becomes difficult. Recent studies indicate that students are less likely to cheat if presented with direct warning messages prior to engaging in online activities. In this work, we present Bluejeans codE Leak deTection (BELT), a system that monitors online BlueJeans meetings for shared code and sends timely warning messages to meeting participants. To test BELT's robustness as an online proctor, we evaluate its code-text disambiguation, code detection from images of varying quality, and code detection from videos of varying resolution. We conclude this work by pinpointing areas of improvement and briefly discuss possible extensions for future work.\",\"PeriodicalId\":20608,\"journal\":{\"name\":\"Proceedings of the Seventh ACM Conference on Learning @ Scale\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Seventh ACM Conference on Learning @ Scale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3386527.3406727\",\"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/10.1145/3386527.3406727","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
As online educational programs scale, monitoring peer collaboration in platforms like BlueJeans for plagiarism becomes difficult. Recent studies indicate that students are less likely to cheat if presented with direct warning messages prior to engaging in online activities. In this work, we present Bluejeans codE Leak deTection (BELT), a system that monitors online BlueJeans meetings for shared code and sends timely warning messages to meeting participants. To test BELT's robustness as an online proctor, we evaluate its code-text disambiguation, code detection from images of varying quality, and code detection from videos of varying resolution. We conclude this work by pinpointing areas of improvement and briefly discuss possible extensions for future work.
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