• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.56-62
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• 2023

In the educational environment of the post-COVID-19 era, metaverse learning, which can improve the disadvantages of online learning and improve learning outcomes, is attracting attention. Metaverse is expected to play an important role as a learning experience platform (LXP) that can provide immersion and experience for learners. In order to successfully introduce and utilize metaverse learning that utilizes the metaverse platform, teachers' knowledge of metaverse-related technologies and pedagogical convergence is important. So far, teacher knowledge for educational use of the metaverse has not been explored. In this regard, this study explored the TPACK (Technological, Pedagogical And Content Knowledge) framework as a teacher's knowledge system for metaverse learning. Based on this, this study designed the class contents of metaverse learning. The results of this study are expected to diffuse the importance of TPACK required for metaverse learning and contribute to the development of teachers' competence.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.969-975
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• 2011

The purpose of this study is to investigate the effects of learners' self-motivational attitudes, use of instructional function, and understanding of successful learning on achievement in college e-learning classes. The study analyzed 297 college students' questionnaire about their internet learning attitudes based on how they understand e-learning and use various internet functions for their learning achievement. After factor analyses, the results found that there were three major factors such as self-motivational attitude, use of instructional function, and understanding of successful learning out of 15 survey items. Multiple regression showed that the self-motivational factor affects the learning achievement with overall three factors analyses. This result indicates that college e-learning classes should focus on the analysis of learners' self-motivational issues in college e-learning classes. This study suggest that the relationship between learners' e-learning experience and learning achievement should be examined in the near future to show how it affects on learners e-learning class management and their achievement.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.127-133
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• 2005

With the increasing use of the Internet improved Internet technologies as well as web-based applications, the uses of e-Learning have also increased the effectiveness of e-Learning has become one of the most practically and theoretically important issues in both Educational Engineering and Information Systems. This study suggests a research model, based on an e-Learning success model, the relationship of the e-leaner's self-regulated learning strategy and the quality perception of the e-Learning environment. This research model focuses on the learning environment and on self regulated learning strategy. The former consists of LMS, learning contents and interaction that are provided by e-Learning and the latter refers to the learners' self-regulated learning strategy. We will show the validity of the model empirically. As result, most of the hypotheses except for H6 suggested in this model were accepted.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.127-133
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• 2007

The u-learning environment has been developed through a number of iterations, and has now been formally evaluated, through analysis of student learning results and the use of quantitative and qualitative measures, Generally, for advance learning effect and analysis of student learning results, the most learning system be use to the item analysis method. But, nowadays, it has using the IRT(Item Response Theory) instead of the item analysis method, The IRT adopts explicit models for the probability of each possible response to a test. Therefore, I proposed the lightweight component based u-learning system using the IRT. Applied device of u-learning is PDA which is in Windows mobile 5.0 environments.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.221-228
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• 2006

With the increasing use of the Internet improved Internet technologies as well as web-based applications, the effectiveness assessment of e-Learning has become one of the most practically and theoretically important issues in both Educational Engineering and Information Systems. This study suggests a research model, based on an e-Learning success model, the relationship of the e-learner's self-regulated learning strategy and the quality perception of the e-Learning environment. This research model focuses on the learning environment and on e-learning strategy. The former consists of learning management system, learning content quality and service quality that are provided by e-Loaming. The latter refers to the learners' self-regulated learning strategy. We will show the validity of the model empirically.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.159-167
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• 2008

The purpose of this study is finding factors affecting the students' use of the Cyber Home Learning Service System and exploring the direction of this system. It is based on the TAM(Technology Acceptance Model) and the result of the previous studies, six external and three internal factors influencing the sue of Cyber Home Learning Service System were extracted. The participants were 201 elementary school students in Pusan. The response of the questionnaire was gathered by online survey system. To analyze the data and the hypothesis, multiple regression and factor analysis were explored. The result indicated that (a) "usefulness" and "future-intention" affected statically to the use, (b) "usefulness" to the future-intention, (c) "subjective judgement", "fun", and "ease of use" to the usefulness, (d) "self-efficiency" and "contents quality" to the ease of use.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.57-65
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• 2020

At present, the existing virus recognition systems usually use signature approach to detect malicious executable files, but these methods often fail to detect new and invisible malware. At the same time, some methods try to use more general features to detect malware, and achieve some success. Moreover, machine learning-based approaches are applied to detect malware, which depend on features extracted from malicious codes. However, the different distribution of features oftraining and testing datasets also impacts the effectiveness of the detection models. And the generation oflabeled datasets need to spend a significant amount time, which degrades the performance of the learning method. In this paper, we use transfer learning to detect new and previously unseen malware. We first extract the features of Portable Executable (PE) files, then combine transfer learning training model with KNN approachto detect the new and unseen malware. We also evaluate the detection performance of a classifier in terms of precision, recall, F1, and so on. The experimental results demonstrate that proposed method with high detection rates andcan be anticipated to carry out as well in the real-world environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.127-133
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• 2019

Most studies of software fault prediction have been about supervised learning models that use only labeled training data. Although supervised learning usually shows high prediction performance, most development groups do not have sufficient labeled data. Unsupervised learning models that use only unlabeled data for training are difficult to build and show poor performance. Semi-supervised learning models that use both labeled data and unlabeled data can solve these problems. Self-training technique requires the fewest assumptions and constraints among semi-supervised techniques. In this paper, we implemented several models using self-training algorithms and evaluated them using Accuracy and AUC. As a result, YATSI showed the best performance.

• Journal of radiological science and technology
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• v.41 no.5
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• pp.487-491
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• 2018

The purpose of this study was to analyze questionnaires of 161 college students attending radiology departments in order to investigate the actual condition of internet use of radiology students. As a result, 95% of college students using the Internet showed 5.8% of general knowledge, 56.9% of radiation major, and 45.8% of general education. In the field of Internet use, basic medicine was 71.2%, anatomy 59.5% and physiology 51.6%. Radiation theory was 39.9% in radiation physics, 31.4% in radiation biology, and 18.3% in radiation management. The radiological applications were followed by radiography and radiography in order of 31.4% and 20.3%, respectively. The radiological imaging was 45.8%, MRI was 37.9%, CT was 37.3%, ultrasound was 24.2%, And radiation nuclear medicine 25.5%. The results of the descriptive statistics of the satisfaction of the contents using the Internet media showed that the overall satisfaction was below 2.5 Based on the results of this study, it is necessary to develop a program with high accessibility to provide various opportunities for internet-based opportunities to increase the academic achievement value of major subjects through the internet and to solve the difficulties in the major subject.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.169-174
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• 2019

In this study, I use surveillance data collection and data mining, clustered by clustering method, and use supervised learning to judge similarity. I aim to use feature extraction algorithms and supervised learning to analyze the suitability of the correlations of personality. After conducting the questionnaire survey, the researchers refine the collected data based on the questionnaire, classify the data sets through the clustering techniques of WEKA, an open source data mining tool, and judge similarity using supervised learning. I then use feature extraction algorithms and supervised learning to determine the suitability of the results for personality. As a result, it was found that the highest degree of similarity classification was obtained by EM classification and supervised learning by Naïve Bayes. The results of feature classification and supervised learning were found to be useful for judging fitness. I found that the accuracy of each Big-5 personality was changed according to the addition and deletion of the items, and analyzed the differences for each personality.