• The Journal of Information Systems
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• v.29 no.4
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• pp.45-56
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• 2020

Purpose The purpose of this study is to present the direction of efficient e-learning education through the importance and satisfaction survey of learners of non-face-to-face video lectures. Therefore, by grasping the degree of satisfaction of the importance ratio through the IPA analysis method, we try to present improvement measures for insufficient education methods. Design/methodology/approach For IPA analysis, we conducted an online survey of four universities and analyzed 154 samples. The analysis method used SPSS, and through the wordcloud analysis method of R, the suggestions for the non-face-to-face lecture method felt by learners were analyzed to derive implications for improving the quality of education. Findings As a result of the overall satisfaction survey for the entire non-face-to-face class, the factors with the greatest dissatisfaction are listed as follows. Complaints about the adequacy of learning materials and activities (quiz, discussion, assignments, etc.), Complaints about how to use the produced content, and complaints about announcements about class management (lecture schedule, lecture method) were identified in order. The factors of dissatisfaction were clear in the non-face-to-face class where interactive communication was impossible or insufficient. In addition to the lack of quick Q&A, there seems to have been a phenomenon of some neglect.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.995-1006
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• 2022

The purpose of this study was to investigate the current status of practical course operation in a non-face-to-face online environment, learning effects, and students' experiences and perceptions for radiology major students using a survey. The questionnaire consisted of a total of 34 items in 5 areas: general characteristics of subjects, current learning participation in non-face-to-face environments, learning satisfaction, learning outcomes, improvement and requirements. For the analysis of the questionnaire responses, frequency analysis was performed on the response frequency, ratio, and scale for each item. Based on the general characteristics of the survey respondents, cross-analysis was performed using the chi-square test for participation in non-face-to-face learning, learning performance, and learning satisfaction. implemented. Improvements and requirements were qualitatively analyzed for the repetition frequency of words with the same meaning. Through the results of analyzing the responses of a total of 397 questionnaires, the direction of design and development of practical classes in a non-face-to-face environment in the future and basic information and implications for efficient operation were confirmed. Based on this, it is necessary to continue to think and make efforts for the efficient operation of non-face-to-face practice classes in the post-corona era.

• Journal of Industrial Convergence
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• v.19 no.6
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• pp.123-129
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• 2021

This Study is to identify factors that affect the interaction between professors and university students and their satisfaction with non-face-to-face lectures. The subjects were 348 university student who attended from October 5 to October 23, 2020. The statistics program was SPSS win 22.o. Among the expected benefits of non-face-to-face classes, the temporal benefit was 3.69 points, the expected benefit of the learning effect was 3.46 points, and the technical familiarity was 3.47 points. Satisfaction with non-face-to-face classes was found to be 3.58 points. Factors affecting the satisfaction of lectures in non-face-to-face classes were expected benefits of learning effect, university satisfaction, technical familiarity, expected benefits over time, and the number of non-face-to-face classes desired for the next semester. Learning effect The higher the expected benefit, the higher the university satisfaction, the higher the technical familiarity, the higher the expected temporal benefit, the higher the number of non-face-to-face classes desired for the next semester, the higher the satisfaction with the non-face-to-face class lectures. Therefore, it was confirmed that the role of the instructor was important in the interaction between the instructor and university students in the non-face-to-face class and the satisfaction of the lecture.

• Journal of Christian Education in Korea
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• v.69
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• pp.139-162
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• 2022

This study verified the effect of learning motivation on immersion by non-face-to-face class method. For this purpose, 101 college students majoring in early childhood education were selected as research subjects. The average age of the study subjects was 22.6 years old, and 51 students took non-real-time non-face-to-face classes, and 50 students took real-time non-face-to-face classes. The study measured the level of immersion and the type of learning motivation after the non-face-to-face class was finished. The measured data were analyzed using descriptive statistical analysis and multiple regression analysis. As a result, in the results for all students, the performance approach goal had the most influence on immersion, and the mastery goal orientation had the next effect. Performance avoidance orientation had no effect. For students in non-face-to-face classes, performance approach goal orientation had an effect on immersion, and for students in real-time non-face-to-face classes, mastery goal orientation had an effect. The implications that can be obtained from the results of this study are as follows. First, non-real-time non-face-to-face classes should cover basic knowledge and skills so that there are no mistakes and failures. Second, non-real-time non-face-to-face classes should allow tasks with appropriate difficulty to be performed with a deadline. Third, real-time non-face-to-face classes should lower the fear of mistakes and failures.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.111-119
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• 2022

This study examined the relationship of self-directed learning ability, teaching presence, learning flow and academic achievement of nursing students in non-face-to-face class. Data were collected through self reported structured questionnaire in 146 nursing students from June 19 to 20, 2021. Data were analyzed using SPSS/WIN 23.0. As a result this study, academic achievement was positively correlated with self-directed learning ability, teaching presence and learning flow. As result regression analysis, learning flow, self-directed learning ability and teaching presence explained 67% of the academic achievement in nursing students. Therefore, based on this study it will be more effective to improve academic achievement of instructional design and customized instructional methods that can improve self-directed learning ability, teaching reality and learning commitment are applied together in non-face-to-face classes.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.453-461
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• 2020

This is a study to investigate the factors affecting the Self-control in COVID-19 non-face-to-face online learning participate and to present a strategy for effective program development. The subjects of this study were 105 participants of COVID-19 non-face-to-face online learning participate in J-do area and collected data by self-reported questionnaire. Data were analyzed by t-test, ANOVA, correlation analysis and stepwise multiple regression analysis. The results of this study were the explanatory power was 50.7% with self-control, self-regulation efficacy, self-confidence. As a result of the above, in order to improve the self-control of participants in Multiple disaster situations non-face-to-face online offline learning, it is necessary to develop a fundamental and continuous educational program that improves the self-regulation efficacy and confidence of learning participants.

• Journal of Science Education
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• v.45 no.2
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• pp.257-273
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• 2021

This study explores classes for pre-service mathematics teachers. The purpose of this study is to examine the differences between 'non-face-to-face' classes & 'face-to-face' classes, as well as the differences in learning outcomes between these two methods. A Professors' Learning Group was formed to effectively carry out this study. Through this learning group, we shared how to plan and operate the lecture. The subjects of this study are 'non-face-to-face calculus courses & face-to-face calculus courses' and 'non-face mathematics education theory courses & face-to-face mathematics education theory courses." Specifically, in these two pairs of courses, we analyze the differences in course management and the differences in the outcomes of students' assessments. Non-face-to-face classes were planned, developed, implemented and evaluated based on the 'non-face class design model.' The results of this study are as follows: First, we explored the differences between 'non-face-to-face classes/mixed classes' and 'face-to-face classes.' Second, the achievement results in calculus courses were higher in face-to-face classes than in non-face classes. Third, the results of achievements in mathematics education theory courses were higher in mixed classes than in face-to-face classes. Through the results of this study, we hope that the non-face-to-face class capabilities can be improved in pre-service mathematics teacher training.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.63-68
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• 2020

This paper proposes a face authentication system based on deep learning framework. The proposed system is consisted of face region detection and feature extraction using deep learning algorithm, and performed the face authentication using joint-bayesian matrix learning algorithm. The performance of proposed paper is evaluated by various face database , and the face image of one person consists of 2 images. The face authentication algorithm was performed by measuring similarity by applying 2048 dimension characteristic and combined Bayesian algorithm through Deep Neural network and calculating the same error rate that failed face certification. The result of proposed paper shows that the proposed system using deep learning and joint bayesian algorithms showed the equal error rate of 1.2%, and have a good performance compared to previous approach.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.66-72
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• 2006

In face recognition, learning speed of face is very important since the system should be trained again whenever the size of dataset increases. In existing methods, training time increases rapidly with the increase of data, which leads to the difficulty of training with a large dataset. To overcome this problem, we propose SVDD (Support Vector Domain Description)-based learning method that can learn a dataset of face rapidly and incrementally. In experimental results, we show that the training speed of the proposed method is much faster than those of other methods. Moreover, it is shown that our face recognition system can improve the accuracy gradually by learning faces incrementally at real environments with illumination changes.