{"title":"Application of computer vision technologies for automatic data collection about emotions of students during group work","authors":"R. Kupriyanov","doi":"10.32517/0234-0453-2020-35-5-56-63","DOIUrl":null,"url":null,"abstract":"Today the global scientific community is actively discussing the issues on the application of artificial intelligence in education. One of the least studied technologies in terms of its application in education is computer vision. The development and implementation of intelligent systems based on video analysis and machine learning algorithms provide new opportunities for teachers and staff of the educational organization administration to understand and transform the educational process. The article discusses the use of video analysis technologies from cameras with a 360-degree view to collect data on the emotional state of students during group work in the classroom. In the course of the described research, a software solution for automatic emotions data collection during students’ teamwork leaning was developed. This solution can be used for future research aimed at studying the impact of emotional state on students’ educational success. The results of the study can be used to form the research agenda of Russian universities in order to implement the objectives of the section “Education and personnel” of the program “Digital economy of the Russian Federation”, approved by the Government of the Russian Federation.","PeriodicalId":45270,"journal":{"name":"Informatics in Education","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2020-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Informatics in Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32517/0234-0453-2020-35-5-56-63","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 1
Abstract
Today the global scientific community is actively discussing the issues on the application of artificial intelligence in education. One of the least studied technologies in terms of its application in education is computer vision. The development and implementation of intelligent systems based on video analysis and machine learning algorithms provide new opportunities for teachers and staff of the educational organization administration to understand and transform the educational process. The article discusses the use of video analysis technologies from cameras with a 360-degree view to collect data on the emotional state of students during group work in the classroom. In the course of the described research, a software solution for automatic emotions data collection during students’ teamwork leaning was developed. This solution can be used for future research aimed at studying the impact of emotional state on students’ educational success. The results of the study can be used to form the research agenda of Russian universities in order to implement the objectives of the section “Education and personnel” of the program “Digital economy of the Russian Federation”, approved by the Government of the Russian Federation.
期刊介绍：
INFORMATICS IN EDUCATION publishes original articles about theoretical, experimental and methodological studies in the fields of informatics (computer science) education and educational applications of information technology, ranging from primary to tertiary education. Multidisciplinary research studies that enhance our understanding of how theoretical and technological innovations translate into educational practice are most welcome. We are particularly interested in work at boundaries, both the boundaries of informatics and of education. The topics covered by INFORMATICS IN EDUCATION will range across diverse aspects of informatics (computer science) education research including: empirical studies, including composing different approaches to teach various subjects, studying availability of various concepts at a given age, measuring knowledge transfer and skills developed, addressing gender issues, etc. statistical research on big data related to informatics (computer science) activities including e.g. research on assessment, online teaching, competitions, etc. educational engineering focusing mainly on developing high quality original teaching sequences of different informatics (computer science) topics that offer new, successful ways for knowledge transfer and development of computational thinking machine learning of student''s behavior including the use of information technology to observe students in the learning process and discovering clusters of their working design and evaluation of educational tools that apply information technology in novel ways.
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