{"title":"智能辅导系统中笔记对学习过程的影响","authors":"Ines Šarić-Grgić, Ani Grubišić, Branko Žitko","doi":"10.2478/acss-2021-0004","DOIUrl":null,"url":null,"abstract":"Abstract The research investigates how note-taking practice affects the learning process in Tutomat, an intelligent tutoring system. The complete analysis includes (i) the identification of learning analytics variables to describe student-Tutomat interaction; (ii) the description of experimental student groups using learning analytics variables; (iii) data-driven clustering and (iv) the comparison of the experimental groups and revealed clusters. The results show that there is a difference in how a student interacts with Tutomat based on note-taking practice. It is revealed that the note-taking practice can be detected using the proposed learning analytics variables with the prediction accuracy of the clustering approach of 85 %.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"26 1","pages":"31 - 37"},"PeriodicalIF":0.5000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The Impact of Note-Taking on the Learning Process in Intelligent Tutoring System Tutomat\",\"authors\":\"Ines Šarić-Grgić, Ani Grubišić, Branko Žitko\",\"doi\":\"10.2478/acss-2021-0004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The research investigates how note-taking practice affects the learning process in Tutomat, an intelligent tutoring system. The complete analysis includes (i) the identification of learning analytics variables to describe student-Tutomat interaction; (ii) the description of experimental student groups using learning analytics variables; (iii) data-driven clustering and (iv) the comparison of the experimental groups and revealed clusters. The results show that there is a difference in how a student interacts with Tutomat based on note-taking practice. It is revealed that the note-taking practice can be detected using the proposed learning analytics variables with the prediction accuracy of the clustering approach of 85 %.\",\"PeriodicalId\":41960,\"journal\":{\"name\":\"Applied Computer Systems\",\"volume\":\"26 1\",\"pages\":\"31 - 37\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2021-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Computer Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/acss-2021-0004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computer Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/acss-2021-0004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
The Impact of Note-Taking on the Learning Process in Intelligent Tutoring System Tutomat
Abstract The research investigates how note-taking practice affects the learning process in Tutomat, an intelligent tutoring system. The complete analysis includes (i) the identification of learning analytics variables to describe student-Tutomat interaction; (ii) the description of experimental student groups using learning analytics variables; (iii) data-driven clustering and (iv) the comparison of the experimental groups and revealed clusters. The results show that there is a difference in how a student interacts with Tutomat based on note-taking practice. It is revealed that the note-taking practice can be detected using the proposed learning analytics variables with the prediction accuracy of the clustering approach of 85 %.