Takanori Maruichi, Taichi Uragami, Andrew W. Vargo, K. Kise
{"title":"Handwriting behavior as a self-confidence discriminator","authors":"Takanori Maruichi, Taichi Uragami, Andrew W. Vargo, K. Kise","doi":"10.1145/3410530.3414383","DOIUrl":null,"url":null,"abstract":"Receiving feedback based on the combination of self-confidence and correctness of an answer can help learners to improve learning efficiency. In this study, we propose a self-confidence estimation method using a simple touch up/move/down events that can be measured in a classroom environment. We recorded handwriting behavior during the answering vocabulary questions with a tablet and a stylus pen, estimating self-reported confidence. We successfully built a method that can predict the user's self-confidence with a maximum of 73% accuracy.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"79 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3410530.3414383","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Receiving feedback based on the combination of self-confidence and correctness of an answer can help learners to improve learning efficiency. In this study, we propose a self-confidence estimation method using a simple touch up/move/down events that can be measured in a classroom environment. We recorded handwriting behavior during the answering vocabulary questions with a tablet and a stylus pen, estimating self-reported confidence. We successfully built a method that can predict the user's self-confidence with a maximum of 73% accuracy.