{"title":"A sensor-assisted model for estimating the accuracy of learning retention in computer classroom","authors":"Jan-Pan Hwang, Ting-Ting Wu, Fu-Jou Lai, Yueh-Min Huang","doi":"10.1109/ICSENST.2011.6137063","DOIUrl":null,"url":null,"abstract":"Modern lifestyle is closely associated with information technology, and sensor technology is particularly used, especially in learning and education. This study proposed a sensor-assisted learning system using sensor technology, in order to determine the learning retention of learners in the learning process, and further provide assistance or feedback. The identification rule of this system is constructed based on decision tree algorithm ID3 (C4.5). The system determined the learning retention according to the learners' visual attention recognition, sitting position variability, and physiological signals analysis.","PeriodicalId":202062,"journal":{"name":"2011 Fifth International Conference on Sensing Technology","volume":"116 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Fifth International Conference on Sensing Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSENST.2011.6137063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Modern lifestyle is closely associated with information technology, and sensor technology is particularly used, especially in learning and education. This study proposed a sensor-assisted learning system using sensor technology, in order to determine the learning retention of learners in the learning process, and further provide assistance or feedback. The identification rule of this system is constructed based on decision tree algorithm ID3 (C4.5). The system determined the learning retention according to the learners' visual attention recognition, sitting position variability, and physiological signals analysis.