J. Yeh, Craig W. Bartholio, Elyse Shackleton, Levi Costello, Matthew Perera, Kyle Yeh, Chelsea Yeh
{"title":"Environmentally Embedded Internet-of-Things for Secondary and Higher Education","authors":"J. Yeh, Craig W. Bartholio, Elyse Shackleton, Levi Costello, Matthew Perera, Kyle Yeh, Chelsea Yeh","doi":"10.1109/ICICT50521.2020.00092","DOIUrl":null,"url":null,"abstract":"In this paper, we describe the application of the technologies of the Internet of Things (IoT) to secondary and higher education. Specifically, it seeks to monitor the status of the students and estimate student attention and engagement based on data collected by environmentally embedded IoT sensors, and provide real-time feedback to the instructor. The research was divided into three parts. The first part researched the IoT sensors and devices used to capture student physiological data. The second part developed the instructor feedback devices, which included tablet and smartwatch applications. The third part is the creation of the machine learning model that performed the estimation of student attention from the data.","PeriodicalId":445000,"journal":{"name":"2020 3rd International Conference on Information and Computer Technologies (ICICT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 3rd International Conference on Information and Computer Technologies (ICICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICT50521.2020.00092","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we describe the application of the technologies of the Internet of Things (IoT) to secondary and higher education. Specifically, it seeks to monitor the status of the students and estimate student attention and engagement based on data collected by environmentally embedded IoT sensors, and provide real-time feedback to the instructor. The research was divided into three parts. The first part researched the IoT sensors and devices used to capture student physiological data. The second part developed the instructor feedback devices, which included tablet and smartwatch applications. The third part is the creation of the machine learning model that performed the estimation of student attention from the data.