K. Umezawa, M. Nakazawa, Manabu Kobayashi, Yutaka Ishii, Michiko Nakano, S. Hirasawa
{"title":"Detection of Careless Mistakes during Programming Learning using a Simple Electroencephalograph","authors":"K. Umezawa, M. Nakazawa, Manabu Kobayashi, Yutaka Ishii, Michiko Nakano, S. Hirasawa","doi":"10.1109/ICCSE49874.2020.9201772","DOIUrl":null,"url":null,"abstract":"There are several difficulties encountered by learners during learning such as good or bad learning content, the difficulty level of learning content, and the degree of learning proficiency. It is possible to detect these difficulties by measuring the browsing history, editing history, and biological information such as brain waves or eye-tracking information. In this paper, we measure electroencephalograph (EEG) information during programming learning. We focus on the relationship between task response time and EEG, and try to detect careless mistakes due to the lack of attention. The results show that careless mistakes during programming learning can be detected by experiments.","PeriodicalId":350703,"journal":{"name":"2020 15th International Conference on Computer Science & Education (ICCSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 15th International Conference on Computer Science & Education (ICCSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSE49874.2020.9201772","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
There are several difficulties encountered by learners during learning such as good or bad learning content, the difficulty level of learning content, and the degree of learning proficiency. It is possible to detect these difficulties by measuring the browsing history, editing history, and biological information such as brain waves or eye-tracking information. In this paper, we measure electroencephalograph (EEG) information during programming learning. We focus on the relationship between task response time and EEG, and try to detect careless mistakes due to the lack of attention. The results show that careless mistakes during programming learning can be detected by experiments.