• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.155-160
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• 2012

This paper designed a smartphone application with sequence problems which users can improve their learning ability and this application is implemented as serious game which is designed for the special purposes of education with entertainment and game-like fun at anytime and anywhere during the spare time. Also to prove learning effects through sequence of number application under ubiquitous environment which is popular these days, the proposed serious game which has various types of sequence questions is implemented based on the iphone and android environments. User characteristics and learning effects which are based on game record of proposed application are analyzed according to socio-demographic characteristics.

• International Journal of Knowledge Content Development & Technology
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• v.5 no.2
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• pp.103-122
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• 2015

This study examined student satisfaction with e-learning experiences in order to determine which factors had the greatest impact on reports of satisfaction among students at Konkuk University. We surveyed 4,889 students enrolled in e-learning courses and analyzed 830 completed questionnaires to identify factors that influence student satisfaction with e-learning. Results showed significant correlations between system factors and satisfaction ($R^2=0.577$; p = 0.000). The system factor with the greatest impact on satisfaction was course attendance rate (0.224; p = 0.000).

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.97-106
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• 2007

According to the 2004 Sloan Consortium Report, distance education is the fastest growing sector of higher education. This study suggests a research model, based on an e-Learning success model, the relationship of the e-learner's self-regulated learning strategy, computer self-efficacy, and system quality perception of the e-Learning environment. As a result, perceived usefulness, perceived ease of use, and service quality effect on e-learner's satisfaction. In addition to, self-regulated learning strategy based on computer self-efficacy is also important variable regarding e-learner's satisfaction.

• Informatization Policy
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• v.31 no.1
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• pp.32-52
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• 2024

This study investigates the effectiveness of virtual reality agents as foreign language instructors with focus on the impact of different native language backgrounds and instructional roles. The agents were first distinguished as native or non-native speakers treated as a between-subject factor, and then assigned roles as either teachers or salespersons considered within-subject factors. An immersive virtual environment was developed for this experiment, and a 2×2 mixed factorial design was carried out. In an experimental group of 72 university students, statistically significant interactions were found in learning satisfaction, memory, and recall between the native/non-native status of the agents and their roles. With regard to learning confidence and presence, however, no statistically significant differences were observed in both interaction effects and main effects. Contextual learning in a virtual environment was found to enhance learning effectiveness and satisfaction, with the nativeness and the role of agents influencing learners' memory; thus highlighting the effectiveness of using virtual reality agents in foreign language learning. This suggests that varied approaches can have positive cognitive and emotional impacts on learners, thereby providing valuable theoretical and empirical implications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2012-2027
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• 2019

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.212-217
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• 2019

This study is a convergence study attempted to understand the learning effects of oral health education class applying action learning on the communication ability and problem-solving ability in dental hygiene students. The subjects of this study were 37 students in the third year of dental hygiene department. As a result, the learning effects of oral health education class applying action learning on the communication ability(p<0.001) and problem-solving ability(p<0.001) showed positive changes in the pre and post comparison. The changes in the scores of sub-dimensions of the communication ability and problem-solving ability were also significant in the pre and post comparison. As the class applying action learning is effective in improving the learner's communication ability and problem-solving ability, it should be utilized as the leaner participation-oriented teaching method for design and operation of dental hygiene education.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.588-592
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• 2015

Many studies have affirmed a negative influence of fragmentation on learning and knowledge sharing in construction projects. However, the literature overlooked enablers of learning within this context. The purpose of this paper is to explore the factors that facilitate project learning and ways to negate any unbecoming effects of fragmentation. Qualitative study used to explore the enablers through interviews administered to 11 top management individuals working in different construction projects in Malaysia. The findings revealed the following factors: participation, relationships, togetherness, and roles of project leader and coordinator. The role of boundary objects was also highlighted including information technology (IT), contract and procedures, drawings, specifications, and reports. The outcome of this paper initiates the development of a model for better knowledge creation and sharing in construction projects. The significance of this model stems from its ability to connection both the characteristics of construction project and project learning theories using the enablers. It is envisaged that future work will be to confirm the model in a quantitative study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1903-1908
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• 2008

The present time, their affect on modern teaching-learning methodology, especially that of cooperative learning is most noticeable through E-learning, ICT(Information Communication Technology), application of Computer, and the Internet. This paper evaluate and analyze a cooperation with computer which affects to the achievement degree for studying. Especially, this paper analyzes the degree and duration of computer usage, and then analyzes the ripple effects on the individuals positivity and participation. And, this paper estimate the different of students' scholastics attainment according to sex, environment and computer usage ability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.670-673
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• 2020

Recent advances in artificial intelligence have allowed for easier sentiment analysis (e.g. positive or negative forecast) of documents such as a finance reports. In this paper, we investigate a method to apply text mining techniques to extract in the financial report using deep learning, and propose an accounting model for the effects of sentiment values in financial information. For sentiment analysis with keyword detection in the financial report, we suggest the input layer with extracted keywords, hidden layers by learned weights, and the output layer in terms of sentiment scores. Our approaches can help more effective strategy for potential investors as a professional guideline using sentiment values.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2310-2332
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• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.