• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.695-704
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• 2021

This study aims to determine the effect of changes in learning systems and its effects on students' learning outcomes amid the Covid-19 pandemic. The sample of this study are the students who are in Jakarta, Indonesia. "Non-probability random sampling" technique has been used to select the samples while the sampling method used is "purposive sampling", where criteria are used to select samples. The samples in this study are 200 people taken randomly using Google Form. Concentration ability and learning interest can affect learning outcomes with the mediation of learning comfort and a good learning environment. As well as physical distancing can moderate the effect of concentration ability and learning interest on learning outcomes. The ability to concentrate on improving learning outcomes requires psychomotor improvement. Whereas interest in learning with indicators of learning awareness can improve learning outcomes. A clean environment is a strength in the learning comfort and the community environment can be recommended in the learning environment. The implementation of the restriction of gathering becomes an important point of physical distancing. The other novelties are the learning comfort and the learning environment as mediating variables and physical distancing as moderating variables in one study at a time.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.591-596
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• 2019

In this paper, we implemented a deep learning operation structure with less influence of local PC performance. In general, the deep learning model has a large amount of computation and is heavily influenced by the performance of the processing PC. In this paper, we implemented deep learning operation using AWS and streaming server to reduce this limitation. First, deep learning operations were performed on AWS so that deep learning operation would work even if the performance of the local PC decreased. However, with AWS, the output is less real-time relative to the input when computed. Second, we use streaming server to increase the real-time of deep learning model. If the streaming server is not used, the real-time performance is poor because the images must be processed one by one or by stacking the images. We used the YOLO v3 model as a deep learning model for performance comparison experiments, and compared the performance of local PCs with instances of AWS and GTX1080, a high-performance GPU. The simulation results show that the test time per image is 0.023444 seconds when using the p3 instance of AWS, which is similar to the test time per image of 0.027099 seconds on a local PC with the high-performance GPU GTX1080.

• The Journal of Korean Academic Society of Nursing Education
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• v.3 no.2
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• pp.207-213
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• 1997

Basic science teaching and clinical education should be integrated whenever appropriate, and the development of skills, values, and attitudes which are emphasized to the same extent as the acquisition of knowledge in nursing. Problem-based learning provides a students-centered learning environment and encourages an inquisitive style of learning. The purpose of this paper is to review and comment the role of tutors and students on problem-based learning. The use of problem-based learning places a high demand on faculty members' time and support. The role of tutors in Problem-based learning focuses primarily on issues of developing and teaching the curriculum and on organizational implementation and institutionalization. Tutors are an integral part of course planning. Tutors serve as a constant source of feedback on student needs and concerns to the course director and constitute an informal steering committee while the course is in progress. Tutors write cases, develop student evaluation methods, recommend resources, suggest modifications in lectures and laboratories. Students have a limited amount of time available to study what is traditionally defined as the core content of nursing. But, the role of students in Problem-based learning would be active, independent learners and problem-solvers rather than passive recipients of information. Students using a deep level approach attempt to integrate what they learn with what they already know, to understand the meaning underlying the material to be learned, and to look for explanations rather than facts. Students are encouraged, with appropriate guidance, to define their own learning goals, to select appropriate experiences to achieve these goals, and to be responsible for assessing their own learning progress. Problem-based learning is more flexible and meaningful, by encouraging student interaction, and by having a better emotional climate than the conventional learning.

• The Journal of Korean Association of Computer Education
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• v.10 no.4
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• pp.93-102
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• 2007

The present study is to investigate the elements of e-learning strategies affecting the levels of participation, achievement and satisfaction for learners who participated in the university blended learning environment. For this, 58 subjects were recruited who participated in the blended learning class at K university. E-learning strategies, achievement and satisfaction levels were measured for data collection, and the level of participation was measured by analyzing the LMS log-in database. For data analysis, first, means and standard deviation were computed to find the level of e-learning strategies of the subjects. Second, linear regression analysis was conducted to find the e-learning strategies that could estimate the levels of achievement, participation and satisfaction. As a result, variables to estimate the achievement level included time management strategy and overload management strategy. Variables to estimate the participation level included self-directed strategy, time management strategy and overload management strategy. Finally, variables to estimate the satisfaction level included multiple discussion management strategy, asynchronous management strategy and sociality. Based on these estimated variables, the author suggested some ideas to increase the educational effectiveness.

• Journal of Information Technology Services
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• v.20 no.4
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• pp.129-140
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• 2021

Recently, interest in AI tutors is rising as a way to bridge the educational gap in school settings. However, research confirming the effectiveness of AI tutors is lacking. The purpose of this study is to explore how effective learner characteristics and recommended item learning activities are in predicting learner's academic performance in an adaptive online learning environment. This study proposed the hypothesis that learner characteristics (prior knowledge, midterm evaluation) and recommended item learning activities (learning time, correct answer check, incorrect answer correction, satisfaction, correct answer rate) predict academic achievement. In order to verify the hypothesis, the data of 362 learners were analyzed by collecting data from the learning management system (LMS) from the perspective of learning analytics. For data analysis, regression analysis was performed using the regsubset function provided by the leaps package of the R program. The results of analyses showed that prior knowledge, midterm evaluation, correct answer confirmation, incorrect answer correction, and satisfaction had a positive effect on academic performance, but learning time had a negative effect on academic performance. On the other hand, the percentage of correct answers did not have a significant effect on academic performance. The results of this study suggest that recommended item learning activities, which mean behavioral indicators of interaction with AI tutors, are important in the learning process stage to increase academic performance in an adaptive online learning environment.

• Journal of the Korean Home Economics Association
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• v.50 no.8
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• pp.13-19
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• 2012

The present study explored the hours of living and playtime depending on existence of caregiver after school using the data from the Korean Children and Youth Panel Survey7071. KCYPS date collected on 1th, 4th and 7th grade children and their caregivers. The hours of living was categorized into sleeping, learning, reading and play times. Playtime was sub-categorized into time for using playing the computer/games, watching TV/DVD and playing with peer groups. The present study has shown that children in the fourth grade spent more time on learning, whereas reading children in the seventh grade spent more time on playing compared to those in other grades during the weekdays. Also, children in higher grades spent more time playing both on the weekdays and on the weekends. Students of all grades spent more time watching TV/DVD compared to other activities during the weekdays and the weekends. Children with a caregiver spent more time on learning and spent less time playing computer/game, watching TV/DVD and playing with peer groups than children without caregivers. As students moved up a grade, these results clearly appeared. Considering the results in this study, the allocation of hours of living and playtime of children altered depending on the existence of a caregivers.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.53-60
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• 2023

Recently, the demand and traffic volume for various multimedia contents are rapidly increasing through real-time streaming platforms. In this paper, we predict real-time streaming traffic to improve the quality of service (QoS). Statistical models have been used to predict network traffic. However, since real-time streaming traffic changes dynamically, we used recurrent neural network-based deep learning models rather than a statistical model. Therefore, after the collection and preprocessing for real-time streaming data, we exploit vanilla RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU models to predict real-time streaming traffic. In evaluation, the training time and accuracy of each model are measured and compared.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.285-288
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• 2017

In this paper, we propose a vision-based object detection and tracking system using online learning. The proposed system adopts a feature point-based method for tracking a series of inter-frame movement of a newly detected object, to estimate rapidly and toughness. At the same time, it trains the detector for the object being tracked online. Temporarily using the result of the failure detector to the object, it initializes the tracker back tracks to enable the robust tracking. In particular, it reduced the processing time by improving the method of updating the appearance models of the objects to increase the tracking performance of the system. Using a data set obtained in a variety of settings, we evaluate the performance of the proposed system in terms of processing time.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.870-876
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• 1993

This paper presents a binary clustering network (BCN) and a heuristic algorithm to detect pitch for recognition of keywords in continuous speech. In order to classify nonlinear patterns, BCN separates patterns into binary clusters hierarchically and links same patterns at root level by using the supervised learning and the unsupervised learning. BCN has many desirable properties such as flexibility of dynamic structure, high classification accuracy, short learning time, and short recall time. Pitch Detection algorithm is a heuristic model that can solve the difficulties such as scaling invariance, time warping, time-shift invariance, and redundance. This recognition algorithm has shown recognition rates as high as 95% for speaker-dependent as well as multispeaker-dependent tests.

• Journal of Engineering Education Research
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• v.27 no.2
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• pp.25-34
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• 2024

Due to the Covid-19 pandemic, it seems to have been impossible to run offline training courses. To overcome this situation, online training courses has been emerged. Just moving the educational environment from offline to online instead of re-designing the curriculum, however, is not effective for trainees. To maximize educational effectiveness, it is necessary to re-design the curriculum based on constructivist appoach which gives trainees experience on skills and knowledge about their job. As for re-designing the curriculum into real-time online practical learning based on constructivism, learning satisfaction and work efficacy of trainees may have been increased. From these results, HRD professionals in an IT company should need to consider how to structure the curriculum when they design the real-time online practical learnings.