Georgios A. Dafoulas, Jerome Samuels-Clarke, C. Maia, Almaas A. Ali, Ariadni Tsiakara
{"title":"通过使用生物识别技术提供更智能的学习支持","authors":"Georgios A. Dafoulas, Jerome Samuels-Clarke, C. Maia, Almaas A. Ali, Ariadni Tsiakara","doi":"10.1109/ICT.2019.8798863","DOIUrl":null,"url":null,"abstract":"The Internet of Things (IoT) has evolved into a mainstream keyword describing the use of interconnected device for data transfer. The authors present their current research study that aims at collecting biometric data from learners and using them for providing innovative feedback for a number of learning tasks. The paper discusses how measuring Galvanic Skin Response heartbeat rate and voice patterns can help to provide an alternative type of learner support. The discussion also covers how biometrics data are filtered by applying a number of profiling techniques to classify learners in different groupings. The paper also briefly touches on hardware aspects of the work carried out, as well as analysis of data sets from a student cohort.","PeriodicalId":127412,"journal":{"name":"2019 26th International Conference on Telecommunications (ICT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Offering smarter learning support through the use of biometrics\",\"authors\":\"Georgios A. Dafoulas, Jerome Samuels-Clarke, C. Maia, Almaas A. Ali, Ariadni Tsiakara\",\"doi\":\"10.1109/ICT.2019.8798863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Internet of Things (IoT) has evolved into a mainstream keyword describing the use of interconnected device for data transfer. The authors present their current research study that aims at collecting biometric data from learners and using them for providing innovative feedback for a number of learning tasks. The paper discusses how measuring Galvanic Skin Response heartbeat rate and voice patterns can help to provide an alternative type of learner support. The discussion also covers how biometrics data are filtered by applying a number of profiling techniques to classify learners in different groupings. The paper also briefly touches on hardware aspects of the work carried out, as well as analysis of data sets from a student cohort.\",\"PeriodicalId\":127412,\"journal\":{\"name\":\"2019 26th International Conference on Telecommunications (ICT)\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 26th International Conference on Telecommunications (ICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICT.2019.8798863\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 26th International Conference on Telecommunications (ICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICT.2019.8798863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Offering smarter learning support through the use of biometrics
The Internet of Things (IoT) has evolved into a mainstream keyword describing the use of interconnected device for data transfer. The authors present their current research study that aims at collecting biometric data from learners and using them for providing innovative feedback for a number of learning tasks. The paper discusses how measuring Galvanic Skin Response heartbeat rate and voice patterns can help to provide an alternative type of learner support. The discussion also covers how biometrics data are filtered by applying a number of profiling techniques to classify learners in different groupings. The paper also briefly touches on hardware aspects of the work carried out, as well as analysis of data sets from a student cohort.