Anna-Maria Velentza, I. Lefkos, Nikolaos Fachantidis
{"title":"Human-Robot Co-Teaching in Online University Course during Covid-19","authors":"Anna-Maria Velentza, I. Lefkos, Nikolaos Fachantidis","doi":"10.1109/IISA56318.2022.9904335","DOIUrl":null,"url":null,"abstract":"Online lectures are extensively used in the academic area. Especially due to the Covid-19 lockdown restriction measures, the educational institutions were forced to conduct online classes. Consequently, it is important to determine how these classes can become more enjoyable while at the same time delivering the academic objectives to the students and how academic tutors can optimally interact with students. This paper specifically looks at the performance of social robots in place of university co-tutors, in the field of engineering, measuring the students’ enjoyment and understanding of the basic principles of the lecture’s content. Inspired by previous educational studies which have evidenced beneficial effects for both students and tutors after taught/conducting lectures with two collaborative tutors, the goal of this research is a) to test the students’ evaluation of two collaborative human tutors in comparison with one individual human when teaching academic lectures during online lectures, and b) to investigate the effect of a social robot co-tutor after comparing students understanding and level of enjoyment after attending a lecture given by human-human or human-robot co-tutors. The lectures took place via an online educational platform during an actual university course. Results indicated that students evaluated higher the co-tutor lectures in comparison with the individual tutor lectures, while they equally enjoyed and gained knowledge from both human-human and human-robot cotutored lectures.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IISA56318.2022.9904335","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
Online lectures are extensively used in the academic area. Especially due to the Covid-19 lockdown restriction measures, the educational institutions were forced to conduct online classes. Consequently, it is important to determine how these classes can become more enjoyable while at the same time delivering the academic objectives to the students and how academic tutors can optimally interact with students. This paper specifically looks at the performance of social robots in place of university co-tutors, in the field of engineering, measuring the students’ enjoyment and understanding of the basic principles of the lecture’s content. Inspired by previous educational studies which have evidenced beneficial effects for both students and tutors after taught/conducting lectures with two collaborative tutors, the goal of this research is a) to test the students’ evaluation of two collaborative human tutors in comparison with one individual human when teaching academic lectures during online lectures, and b) to investigate the effect of a social robot co-tutor after comparing students understanding and level of enjoyment after attending a lecture given by human-human or human-robot co-tutors. The lectures took place via an online educational platform during an actual university course. Results indicated that students evaluated higher the co-tutor lectures in comparison with the individual tutor lectures, while they equally enjoyed and gained knowledge from both human-human and human-robot cotutored lectures.