• Journal of Korean Society for Quality Management
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• v.38 no.2
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• pp.161-170
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• 2010

Diffusion of e-learning has been accelerated according as the convenience and effectiveness have been increased rapidly due to the advancement of information technology. However, there has been few studies on systematic evaluation of its performance. SERVQUAL model was applied to evaluate the service quality of a 100% on-line lecture opened in a major Korean university. Two classes, one for 71 undergraduate students, the other for 79 graduate students, were opened for the lecture. The gaps between the expected service and the perceived service scores were compared with respect to sex, age, and e-learning experience. Although the gap score of male and female students were not different significantly, the gap scores among the other comparative groups were different. The perceived score of the older group with more than thirty ages was lower than that of the younger group. It seems that the older group evaluated the score based on the practical use of the subject since they are part-time students with jobs. Also, the perceived score of the group with previous e-learning experience was higher than that of the group with no e-learning experience. It seems that the experienced group evaluated it compared with the previous e-learning satisfaction. As it might be expected, the groups with higher perceived scores had stronger intention to recommend the e-learning lecture to other students.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.4
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• pp.705-716
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• 2014

This study starts from a question about the expectation that government policy reduces the educational gap by introducing digital textbooks. In addition, we try to figure out how to minimize the problems that will occur when digital textbooks are introduced and fully adopted. The purpose of this study is exploring the possibility that suing digital textbook deepens learning gap among students. Researchers are discussed that the gap between 'information' haves' and 'information' have-nots' and between 'the competent user' and 'the incompetent user' increase difference between 'knows' and 'know-nots' on the socio-economic class. These difference are likely to imply that using digital textbook is deepening the educational gap.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.1005-1013
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• 2021

With the prolonged COVID-19, the existing academic gap is widening. The purpose of this study is to provide homeroom teachers with a visual confirmation of the academic achievement gap in grades and classrooms through academic achievement analysis, and to use this to help them design lessons and explore ways to improve the academic achievement gap. The data of students' Korean and math diagnostic evaluation scores at the beginning of the school year were visualized as clusters using the K-means algorithm, and as a result, it was confirmed that a meaningful clusters were formed. In addition, through the results of the teacher interview, it was confirmed that this system was meaningful in improving the academic achievement gap, such as checking the learning level and academic achievement of students, and designing classes such as individual supplementary instruction and level-specific learning. This means that this academic achievement data analysis system helps to improve the academic gap. This study provides practical help to homeroom teachers in exploring ways to improve the academic gap in grades and classes, and is expected to ultimately contribute to improving the academic gap.

• Journal of Engineering Education Research
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• v.22 no.2
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• pp.55-60
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• 2019

Colleges of technology in Japan are characterized by specialized education starting from the first grades aged fifteen, making it particularly important to provide motivation for specialized subjects. The most difficult thing for teachers in the technical college is giving the motivation to a professional education to the lower grades who don't know the technology and engineering. Teachers tried to use and make a suitable example or an education material for their lecture. The generation gap with students makes it difficult for teachers to use examples of objects that students are actually familiar with in their daily life. To compensate for the generation gap with students, we asserted that education for lower grades should adopt the perspectives of students in higher grades. The relative closeness in age of lower and higher grades helps reduce the generation gap with students, which is advantageous in that teachers can share the perspectives of students.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.11a
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• pp.123-123
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• 2007

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.134-138
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• 2023

The field of digital forensics requires good theoretical and practical knowledge, so practitioners should have an in-depth understanding and knowledge of both theory and practical as they need to take decisions which impacts human lives. With the demand and advancements in the realm of digital forensics, many universities around the globe are offering digital forensics programs, but there is a huge gap between the skills acquired by the student's and the market needs. This research work explores the problems faced by digital forensics programs, and provides solution to overcome the gap between the skills acquired by the student's and the market needs using Activity led learning pedagogy for digital forensics programs.

• Journal of Korean Elementary Science Education
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• v.37 no.2
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• pp.173-187
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• 2018

The purpose of this study is to clarify the specific terms of epistemic and epistemological by reviewing the literature on epistemological understanding of science learning, examine the necessity of epistemic discourse analysis based on the view of social epistemology, and provide an exemplar of practical epistemology analysis for elementary children's science learning. The review was conducted in terms of meaning and terminology about epistemic or epistemological approach to science learning, epistemology of/for science, and methodologies for epistemic discourse analysis. As an alternative way of epistemic discourse analysis in science classroom I employed practical epistemology analysis (by Wickman), evidence-explanation continuum (by Duschl), and DREEC diagram (by Maeng et al.). The methods were administered to an elementary science class for the third grade where children observed sedimentary rocks. Through the outcomes of analysis I sought to understand the processes how children collected data by observation, identified evidence, and constructed explanations about rocks. During the process of practical epistemology analysis the cases of four categories, such as encounter, stand-fast, gap, and relation, were identified. The sequence of encounter, stand fast, gap, and relation showed how children observed sedimentary rocks and how they came to learn the difference among the rocks. The epistemic features of children's observation discourse, although different from scientists' discourses during their own practices, showed data-only conversation, evidence-driven conversation, or explanation inducing conversation. Thus I argue even elementary children are able to construct their own knowledge and their epistemic practices are productive.

• Smart Media Journal
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• v.12 no.11
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• Educational Technology International
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• v.18 no.2
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• pp.73-99
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• 2017

The learning analytics dashboard (LAD) is a supporting tool for teaching and learning in its personalized, automatic, and visual aspects. While several studies have focused on the effect of using dashboard on learning achievement, there is a research gap concerning the impacts of learners' characteristics on it. Accordingly, this study attempted to verify the differences in learning achievement depending on learning motivation level (high vs. low) and dashboard intervention (use vs. non-use). The final participants were 231 university students enrolled in a basic statistics course. As a research design, a 2 × 2 factorial design was employed. The results showed that learning achievement varied with dashboard intervention and the interaction effect was significant between learning motivation and dashboard intervention. The results imply that the impact of LAD may vary depending on learner characteristics. Consequently, this study suggests that the dashboard interventions should be offered after careful consideration of individual students' differences, particularly their learning motivation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.204-211
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• 2020

In this paper, we propose an optimal deep learning network architecture for main object location detection through weak supervised learning. The proposed network adds convolution blocks for improving the localization accuracy of the main object through weakly-supervised learning. The additional deep learning network consists of five additional blocks that add a composite product layer based on VGG-16. And the proposed network was trained by the method of weakly-supervised learning that does not require real location information for objects. In addition, Grad-CAM to compensate for the weakness of GAP in CAM, which is one of weak supervised learning methods, was used. The proposed network was tested through the CUB-200-2011 data set, we could obtain 50.13% in top-1 localization error. Also, the proposed network shows higher accuracy in detecting the main object than the existing method.