• Journal of Digital Convergence
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• v.19 no.11
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• pp.53-62
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• 2021

Distance learning (DL) has become a major issue in the educational field with the spread of COVID-19. In order to enhance the satisfaction of DL learners, efforts to cultivate learners' competencies, as well as investment to build IT infrastructure, and activities to support high-quality content provision should be comprehensively considered. Based on a survey of 221 college students, this study verified that digital literacy (knowledge, skill, and mind) and information systems success factors (system, information, and service quality) all positively affect DL satisfaction, in turn, which positively influences on DL continuance. This study is meaningful in that it comprehensively considered learner's ability and IT infrastructure and analyzed the effect on the satisfaction and intention of continuous use of DL. In the future, it is necessary to expand the target of not only college students but also elementary and secondary students and instructors, and to further consider interaction, which is a major factor in the distance learning process.

• Journal of the Korean Society of Systems Engineering
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• v.20 no.spc1
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• pp.90-96
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• 2024

In this paper, we propose a technique to model high resolution seafloor topography with 1m intervals using actual water depth data near the east coast of the Korea with 1.6km distance intervals. Using a feature point extraction algorithm that harris corner based on deep learning, the location of the center of seafloor mountain was calculated and the surrounding topology was modeled. The modeled high-resolution seafloor topography based on deep learning was verified within 1.1m mean error between the actual warder dept data. And average error that result of calculating based on deep learning was reduced by 54.4% compared to the case that deep learning was not applied. The proposed algorithm is expected to generate high resolution underwater topology for the entire Korean peninsula and be used to establish a path plan for autonomous navigation of underwater vehicle.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.189-196
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• 2023

This study aims to show that "Computer-assisted language learning (CALL)" and "Mobile-based language learning (MALL)" actually influence education, deviating from the traditional "drill and practice" method in foreign language education and learning due to the development of information and communication technology (IT). Specifically, for first-year college students who have relatively poor English skills and do not feel enough motivation for English learning, I will produce educational video content using multimedia authoring tools and upload it to the e-learning system. Video content is configured to be accessed and utilized through various media such as computers, smartphones, tablets, laptops, etc. Ultimately, an exploration of educational value behind the utilization of IT devices in English language Teaching(ELT) and the Second Language Acquisition (SLA) theory behind effective instructional use of such technology are presented. That is to say, the effectiveness of language learning using information and communication technology (IT) is introduced. The article closes by suggesting how to use computers and mobile media for 'Flipped Learning'.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.461-473
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• 2020

This study examined the effect of using web-based distance program in home health education for nursing college students in COVID-19 special disaster area. The study was carried out according a nonequivalent control group pretest-posttest design. The study subjects were 49 nursing college students from K City, Gyeongsangbuk-do. The web-based distance program was conducted for 2 weeks. The data collection period was from June 1, 2020 to June 12, 2020. Collected data were analyzed using SPSS PC+ 19.0 with the Fisher' exact test, Wilcoxon rank sum test, ANCOVA with pretest value as covariate. The experimental group had significantly different in knowledge related home health nursing, perceived motivation, and learning commitment in comparison to the control group. This suggests that the web-based distance program in the COVID-19 special disaster area can be applied as a way to increase nursing students' knowledge related home health nursing, perceived motivation, and learning commitment.

• The Journal of Information Systems
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• v.16 no.1
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• pp.201-230
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• 2007

Recently, the growth of e-Learning systems and its related information technology has presented a unique challenge for both schools and industry. It would make an extremely phenomenal paradigm shift in the educational method and practice. Methods of assessing the quality of e-teaming services and contents are critical issue in both practice and research. Moreover, many researchers are interested in what qualify factors more affect to the Performance of e-Learning service. Nevertheless, service quality is a construct that is difficult to define and measure. e-Learning services are composed of many factors, and they are more complicated than the traditional education services because they we performed on the distance basis and the many platforms of IT infrastructure. The purposes of our research are to classify the e-Learning service dimension and identify their factors, to develop the measurement of the factors, and finally to test empirically their relationship between the service factors and e-Learning service performance. For the development of the service factors we considered SERVQUAL model and SERVPERF model which were developed in the service marketing area. The SERVQUAL model was more fitted to the e-Learning services than the latter. From that we derived several factors that fit to our research domain, ie, tangibles, access, reliability, credibility, security, responsiveness, assurance, empathy. We combined three factors of them(reliability, credibility, security) into a factor, system stability for the semantic simplicity, and divided responsiveness factor into system operator responsiveness and teacher responsiveness as the entity based dimension classification. In the e-Learning services research, Most researcher are mentioned the quality factors of contents, so we added to two contents quality factors, ie, contents production method and richness of contents itself. We examined the relationship between the service quality factors and e-Learning performance(student satisfaction and service reuse intention). As result three quality factors(contents production method, teacher responsiveness, empathy) significantly affected student satisfaction. To the other performance variable, ie, service reuse intention, the teacher related quality factors(such as teacher responsiveness, assurance, empathy) affected only. In conclusion, even in the on-line distance teaming, the teacher's role md earnestness is as important as ever.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.61-67
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• 2022

In this paper, we propose a model that can convert some of the simulation program resources to a mobile environment. Recently, smart factories that use PLCs as controllers in the manufacturing industry are rapidly becoming widespread. However, in the situation where it is difficult to operate due to the shortage of PLC operation personnel, the actual situation is that a platform for PLC operation education is necessary. Currently most PLC-related educational platforms are based on 2D, which makes accurate learning difficult and difficult. When a simulation program is applied to distance learning in a general PC environment, many elements are displayed on the monitor, which makes screen switching inconvenient. Experiments with the proposed model confirmed that there was no frame deterioration under general circumstances. The average response time by the request frame was 102 ms, and it was judged that the learner was not at the level of experiencing the system delay.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.83-95
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• 2022

This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1105-1112
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• 2020

Gait recognition, as a promising biometric, can be used in video-based surveillance and other security systems. However, due to the complexity of leg movement and the difference of external sampling conditions, gait recognition still faces many problems to be addressed. In this paper, an improved convolutional neural network (CNN) based on Gabor filter is therefore proposed to achieve gait recognition. Firstly, a gait feature extraction layer based on Gabor filter is inserted into the traditional CNNs, which is used to extract gait features from gait silhouette images. Then, in the process of gait classification, using the output of CNN as input, we utilize metric learning techniques to calculate distance between two gaits and achieve gait classification by k-nearest neighbors classifiers. Finally, several experiments are conducted on two open-accessed gait datasets and demonstrate that our method reaches state-of-the-art performances in terms of correct recognition rate on the OULP and CASIA-B datasets.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1319-1325
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• 2021

It is important to remove fog for accurate object recognition and detection during preprocessing because images taken in foggy adverse weather suffer from poor quality of images due to scattering and absorption of light, resulting in poor performance of various vision-based applications. This paper proposes an end-to-end deep learning-based single image de-fogging method using U-Net architecture. The loss function used in the algorithm is a loss function based on Mahalanobis distance with fog features, which solves the problem of domain shifts, and demonstrates superior performance by comparing qualitative and quantitative numerical evaluations with conventional methods. We also design it to generate fog through the VGG19 loss function and use it as the next training dataset.

• The Journal of Korean Association of Computer Education
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• v.8 no.1
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• pp.1-12
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• 2005

In this paper, we analyze the course 'computer' on middle and high schools. and propose new organization of the course to enhance the ability of self-directed learning. We also develop a learning system for new organization, based on self-directed teaching and learning principles. The developed learning system makes students choose the topics according to their interest and advance learning by the schedule they set by themselves. To promote students' participation, the teacher also gives students various learning tasks. Through e-board and Q&A, we also accelerate mutual communication among teachers and students, to do teaching-learning activities vigorously.