Background: Due to the COVID-19 pandemic, untact online classes have expanded in universities. To ensure continuous improvements in the quality of university education, it is important to analyze factors affecting students’ satisfaction with lectures in this untact online environment. Objectives: The aim of this study was to investigate lecture satisfaction among physical therapy students with experience of untact online classes. Design: Questionnaire design. Methods: The study population comprised 124 physical therapy students with experience of participation in untact online classes. We analyzed various factors affecting students’ satisfaction with lectures delivered via untact online classes and the correlations between these factors. Results: In terms of untact educational system quality, the level of satisfaction was significantly lower among students who had experienced more semesters with untact online classes than among those who had experienced fewer semesters with untact online classes (P＜.05). Untact educational service quality, untact educational information quality, untact educational system quality, and lecture satisfaction/recommendation intention showed statistically significant positive correlations (P＜.001). Conclusion: It is necessary to continuously improve online support systems and untact educational service quality to enhance physical therapy students’ satisfaction with lectures delivered via untact online classes.

본고는 비대면 환경에서의 영상번역수업에 학습자중심 교육을 적용한 사례 연구이 다. 학습자가 무엇에 관심과 흥미가 있는지 개인프로필을 조사하였고, ZOOM 소회의 실 기능의 조별활동을 통해 학습자간의 상호작용을 최대한 높이고, 개별 피드백과 조별 피드백, 이클래스 LMS를 통해 학습자와 교수자와의 소통을 활성화한 수업이 되도록 진행하였다. 한 학기 동안 학습자중심으로 설계하고 운영한 영상번역수업에 대한 학습자의 강의평가와 만족도를 살펴보았다. 학습자들이 직접 번역하고 교수자 의 피드백을 받고, 조별 활동의 토론을 통해 서로의 의견을 교환한 뒤 조별 완성본 을 제출하면서 수업에 대한 성취감과 만족감이 높은 것으로 확인되었다.

Non-face-to-face lectures have become a necessity rather than an option since COVID-19, and in order to improve the quality of university education, it is necessary to explore the properties of non-face-to-face lectures and make active efforts to improve them. This study, focusing on this, aims to provide basic data necessary for decision-making for non-face-to-face lecture design by analyzing the relative importance and execution satisfaction of non-face-to-face lecture attributes for professors and students. Based on previous research, a questionnaire was constructed by deriving 4 factors from 1st layer and 17 from 2nd layer attributes of non-face-to-face lectures. A total of 180 valid samples were used for analysis, including 60 professors and 120 students. The importance of the non-face-to-face lecture properties was calculated by obtaining the weights for each stratified element through AHP(Analytic Hierachy Process) analysis, and performance satisfaction was calculated through statistical analysis based on the Likert 5-point scale. As a result of the AHP analysis, both the professor group and the student group had the same priority for the first tier factors, but there was a difference in the priorities between the second tier factors, so it seems necessary to discuss this. As a result of the IPA(Importance Performance Analysis) analysis, the professor group selected the level of interaction as an area to focus on, and it was confirmed that research and investment in teaching methods for smooth interaction are necessary. The student group was able to confirm that it is urgent to improve and invest in the current situation so that the system can be operated stably by selecting the system stability. This study uses AHP analysis for professors and students groups to derive relative importance and priority, and calculates the IPA matrix using IPA analysis to establish the basis for decision-making on future face-to-face and non-face-to-face lecture design and revision. It is meaningful that it was presented.

With the development of technology, e-learning has already become popular. According to the National IT Industry Promotion Agency (2016), the total number of e-learning business owners is 1,639, while the market size is 3.4876 trillion won, meaning that it increased by about 1.5 trillion won compared to the market size of 1.9 trillion in 2008. The utilization rate is 58.7%, which is increasing every year. Currently, college students who are actively using e-learning are Net Generation. The Net Generation means a generation that has encountered and has been using digital since birth (Topscott, 2008). As the Net Generation is familiar with the computer, they can use e-learning services without any difficulty. In addition, according to Kim et al. (2014), college students' computer self-efficacy and the score for e-learning acceptance attitude appeared to be significantly higher Unlike the Net Generation that has been familiar with computers and the Internet since birth, the middle-aged and older generation aged between 40 and 50 is the generation to learn about digital media in order to adapt to the changed age (Lee et al., 2012). Although the middle-aged and older generation is not familiar with the use of the Internet and computers, the proportion of using e-learning by the middle-aged and older generation is increasing recently. In the 50s, the e-learning use rate is only 37%, but it is also the generation that growth rate is highest when compared with last year. Among the reasons for not using the e-learning in the middle-aged and older generation, 'interested but not aware of how to use it' was ranked #1 as 26.9% and 24.9%, respectively. In addition, future intentions to use the e-learning by users who do not use it among the middle-aged and older generation between 40 and 50 were 67.4% and 75.2%, respectively, showing that many non-users had intentions to use the e-learning in the future (National IT Industry Promotion Agency, 2016). This shows that the interest and demand for the e-learning in the middle-aged and older generation is high, but those who are in the generation are having difficulty accessing it. As the current middle-aged and older generation who was born in the baby boomer generation is getting closer to retirement, it has a great interest in preparing for old age. Especially in the aging society like now, the 50s are the ages that distinguish between 'ambiguous middle age' and 'extended old age' and that are the time to prepare for life in old age, and the intention of reemployment of the middle-aged and older generation for the preparation of the later years is gradually increasing. In the case of the middle-aged and older generation who is currently looking for re-first because there is not enough old-age income (Kang, 2016). In addition, subsequent re-employment is mainly done in small micro business, skill-related work, and simple labor. Therefore, there is an increasing demand for the preparation for the re-employment as a civil servant who is available to prepare for his/her old age. However, compared to the younger generations in their 20s and 30s, the middle-aged and older students feel a great deal of difficulty in preparing themselves for the civil service examination because they have not studied for a long time, along with technical difficulties. In terms of the quality of Internet e-learning lecture services, it is expected that there will be differences between the younger generation and the middle-aged and older generation due to technical factors and academic factors. Specifically, adult learners are more mature have more diverse needs and expectations, and before anything else, however, they have a sense of responsibility for home and work compared with regular college students. At the same time, they have a clear desire for learning according to specific goals. It has also been found that there are differences in the level of the use of learning strategies depending on the learners' gender, age, and academic background. Therefore, this study has a purpose of comparing the differences in the e-learning acceptance/satisfaction between the middle-aged and older generation and net generation on the basis of technical factors and academic factors by investigating differences in perception and satisfaction with the e-learning and by analyzing the value satisfaction according to age.