• International conference on construction engineering and project management
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• 2022.06a
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• pp.500-507
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• 2022

COVID-19 pandemic forces college education to be rapidly switched from face-to-face education into remote education. Two inconsistent findings exist in previous study about remote learning. First, studies before COVID-19 pandemic found remote learning is an effective method, which provided students with higher achievement and improved their work-life balance. However, studies showed remote learning during COVID-19 pandemic is not as effective as expected because of technical issues, lack of motivations and even mental health issues. Second, findings from studies about remote learning impacts on workload and productivity during COVID-19 are also inconsistent. Therefore, this study aims to quantitatively measure college students' workload and productivity during COVID-19 of different types of tasks to provide a comprehensive and latest evaluation on remote learning. The findings of this study show remote learning slightly increases college students' total listening and speaking tasks workload, total reading and writing tasks workload. Furthermore, phone call, in-person meeting, online meeting and email workload increase significantly in remote learning. However, productivity for both listening and speaking, reading and writing tasks decreases after remote learning but no significant changes of productivity are found.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.276-284
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• 2021

The purpose of this study is to analyze differences in satisfaction with remote learning after the outbreak of COVID-19 between two-year college students and four-year university students. The analysis results regarding differences in satisfaction according to the types of remote learning were as follows: first, male students had greater satisfaction with the hybrid type of in-person and non-contact lecturers than female students; secondly, two-year college students had a higher level of satisfaction with all the types of remote learning than four-year university students; and thirdly, there were significant differences in students' satisfaction with the hybrid type of in-person and non-contact lectures according to the grades in a four-year university. The study then analyzed differences in satisfaction with the management and content of remote learning, interactions with professors, and exams and found that two-year college students exhibited a greater level of satisfaction than four-year university students. No significant differences were found in satisfaction with remote learning according to the grades both in two-year college students and four-year university students. The findings of the study demonstrate significant differences in overall satisfaction with remote-learning according to college and university types. That is, two-year college students had greater satisfaction with remote learning than four-year university students. It is critical to take into consideration the characteristics of students according to college and university types in the development process in order to raise the effects of remote learning based on these analysis results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.437-456
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• 2021

In the field of remote sensing in Korea, starting in 2017, deep learning has begun to show efficient research results compared to existing research methods. Currently, research is being conducted to apply deep learning in almost all fields of remote sensing, from image preprocessing to applications. To analyze the research trend of deep learning applied to the remote sensing field, Korean domestic journal papers, published until October 2021, related to deep learning applied to the remote sensing field were collected. Based on the collected 60 papers, research trend analysis was performed while focusing on deep learning network purpose, remote sensing application field, and remote sensing image acquisition platform. In addition, open source data that can be effectively used to build training data for performing deep learning were summarized in the paper. Through this study, we presented the problems that need to be solved in order for deep learning to be established in the remote sensing field. Moreover, we intended to provide help in finding research directions for researchers to apply deep learning technology into the remote sensing field in the future.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.693-703
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• 2010

High spectral resolution of hyperspectral data enables analysis of complex natural phenomena that is reflected on the data nonlinearly. Although many manifold learning methods have been developed for such problems, most methods do not consider the spatial correlation between samples that is inherent and useful in remote sensing data. We propose a manifold learning method which directly combines the spatial proximity and the spectral similarity through kernel PCA framework. A gain factor caused by spatial proximity is first modelled with a heat kernel, and is added to the original similarity computed from the spectral values of a pair of samples. Parameters are tuned with intelligent grid search (IGS) method for the derived manifold coordinates to achieve optimal classification accuracies. Of particular interest is its performance with small training size, because labelled samples are usually scarce due to its high acquisition cost. The proposed spatial kernel PCA (KPCA) is compared with PCA in terms of classification accuracy with the nearest-neighbourhood classification method.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.59-73
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• 2021

This study purpose to empirically investigated the effects of Learning Presence perceived by university students at general universities who took Remote Classification in the first semester of 2020 on Learning Outcomes and the mediating effects of Learning Immersion. A total of 293 students were surveyed by conducting an online survey for about a month from Sep. 15, 2020, targeting college students attending general universities in Seoul and Gyeonggi-do. The results of the study are as follows: First, Learning Presence had an effect on Learning Immersion and Learning Outcomes, and Learning Immersion had an effect on Learning Outcomes. Second, Learning Immersion had a mediating effect on the relationship between Learning Presence and Learning Outcomes. This study is meaningful in that it verified the relationship between Learning Presence, Learning Outcomes, and Learning Immersion perceived by college students who took Remote Classification in COVID19 response dimension.

• Journal of the Korean Society of Radiology
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• v.14 no.4
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• pp.405-413
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• 2020

Expectations and interests in blended learning are increasing as universities respond to the educational flow of transition to e-learning. This study analyzed the difference between the satisfaction of students in the first grade of radiology and the general characteristics of the subjects when applying blended learning. First, the satisfaction according to the class type was the highest in blended learning classes at 47.2%, followed by lecture room classes at 30.6% and remote classes at 22.2%. Second, the place where the remote lecture was watched by viewing the remote class according to the general characteristics was the highest at 94.4%. The most common medium for attending the remote class was using a PC, with 72.2%, and there was no significant difference in the remote class viewing method (p>0.05). Third, the appropriateness of the blended learning, "Remote lectures and lecture room lectures were properly conducted," had the highest score of 4.27±0.70. In addition, there was no significant difference in response to the teaching method according to gender and age (p>0.05). Fourth, the technology and system support,'Technical support and system support must be done when taking a remote lecture,' showed the highest score of 3.41±0.96. The lack of communication between professors and students,'In the remote class, communication between professors and students is insufficient' was the lowest with 2.88±1.00. In addition, there was no significant difference in the improvement of class according to gender and age (p>0.05). Through this study, it was intended to serve as a basis for the plans of blended classes and the policies of schools that introduced blended classes.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.355-360
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• 2021

COVID-19 has brought major changes to school education. Although it was attempted to guarantee students' right to learn through romote learning, the limitations of remote learning compared to face-to-face classes were clear. Nevertheless, the method of remote learning is undoubtedly a learning method that needs to be continuously developed in terms of being able to consider separated time and space and enabling learners to learn individually and autonomously. Therefore, in this study, real-time romote learning cases were analyzed at the elementary school stage, and problems in real-time remote classes were discovered and improved through teacher interviews. The problems with real-time remote classes in elementary school unit classes examined through examples are: First, that the proportion of teacher activity is high due to the anxiety of the unfamiliar environment of remote classes, and second, even though it is a real-time interactive class, it It was impossible to provide feedback. As a solution to this, it is necessary to consider the basic class steps (introduction-deployment-organization) and the use of class tools to provide appropriate communication and feedback was suggested.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1581-1587
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• 2022

Recently, deep learning has become more important in remote sensing data processing. Huge amounts of data for artificial intelligence (AI) has been designed and built to develop new technologies for remote sensing, and AI models have been learned by the AI training dataset. Artificial intelligence models have developed rapidly, and model accuracy is increasing accordingly. However, there are variations in the model accuracy depending on the person who trains the AI model. Eventually, experts who can train AI models well are required more and more. Moreover, the deep learning technique enables us to automate methods for remote sensing applications. Methods having the performance of less than about 60% in the past are now over 90% and entering about 100%. In this special issue, thirteen papers on how deep learning techniques are used for remote sensing applications will be introduced.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.303-312
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• 2022

In this study, the importance-performance analysis (IPA) of the content components of remote graduate students was conducted. To this end, an online survey of 221 remote graduate students at J University obtained the following results. First, the importance of content components by area was in the order of learning content, interaction, teaching-learning strategy, and evaluation, and the degree of execution was in the order of teaching-learning strategy, interaction, evaluation, and learning content. Second, it was found that there were significant differences between importance and performance in the four areas of content components: learning content, teaching-learning strategy, interaction, and evaluation. The importance-execution analysis (IPA) was conducted in two dimensions: region-specific and item-specific, and the results are as follows. The learning content was found to be the maintenance area, the teaching-learning strategy and interaction were the key improvement areas, and the evaluation area was the overinvestment area. The results of this study can be used as basic data to diagnose the present of remote graduate school content and to gauge what needs to be improved in the future based on it.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.82-88
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• 2021

In this paper, we propose an automatic detection scheme of work distraction for remote management of telecommuting. The proposed scheme periodically captures two consequent computer screens and generates the difference image of these two captured images. The scheme applies the difference image to our deep learning model and makes a decision of abnormal patterns in the difference image. Our deep learning model is designed with the transfer learning technique of VGG16 deep learning. When the scheme detects an abnormal pattern in the difference image, it hides all texts in the difference images to protect disclosure of privacy-related information. Evaluation shows that the proposed scheme provides about 96% detection accuracy.