• The Journal of Korea Robotics Society
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• v.10 no.2
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• pp.90-95
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• 2015

The main goal of e-learning systems is just-in-time knowledge acquisition. Rule-based e-learning systems, however, suffer from the mesa effect and the cold start problem, which both result in low user acceptance. E-learning systems suffer a further drawback in rendering the implementation of a natural interface in humanoids difficult. To address these concerns, even exceptional questions of the learner must be answerable. This paper aims to propose a method that can understand the learner's verbal cues and then intelligently explore additional domains of knowledge based on crowd data sources such as Wikipedia and social media, ultimately allowing for better answers in real-time. A prototype system was implemented using the NAO platform.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.145-152
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• 2015

We consider an online selective-sample learning problem for sequence classification, where the goal is to learn a predictive model using a stream of data samples whose class labels can be selectively queried by the algorithm. Given that there is a limit to the total number of queries permitted, the key issue is choosing the most informative and salient samples for their class labels to be queried. Recently, several aggressive selective-sample algorithms have been proposed under a linear model for static (non-sequential) binary classification. We extend the idea to hidden Markov models for multi-class sequence classification by introducing reasonable measures for the novelty and prediction confidence of the incoming sample with respect to the current model, on which the query decision is based. For several sequence classification datasets/tasks in online learning setups, we demonstrate the effectiveness of the proposed approach.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.63-66
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• 2018

Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.200-206
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• 2020

Digital storytelling, a 21st century educational tool that combines digital media with storytelling, is considered as a powerful tool to learn foreign languages in an authentic and meaningful way. This study explored cyber university students' perceptions and experiences with a digital storytelling project implemented to enhance their English writing and speaking skills. The results of 9 week-long research indicated that most of the students' attitudes were positive: the digital storytelling project was effective to increase their English skills, learning motivation and interest in learning English. Most students perceived that the process writing approach and videotaping tasks provided them with good opportunities to develop their English writing and speaking skills. Although many students had difficulties selecting a topic and writing a script in the initial stage, they could handle with these challenges with the help of professors' feedback and successfully completed their projects. After completing the project, most students expressed a strong sense of accomplishment. These results suggest that the digital storytelling project effectively met cyber university students' learning needs and facilitate their English learning.

• English Language & Literature Teaching
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• v.10 no.2
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• pp.133-158
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• 2004

The purpose of the present study was to scrutinize computer games that can motivate elementary school students through their interactive "edutainment" effects. The types of elements in computer games that students find interesting as learning media and their impact were studied. The current status of Korean computer games, issues related to learning English, and methods to stimulate the motivation and interest in learning by elementary school students were explored. A computer game model for efficiently teaching English to elementary school students through a connection between computer games and education was suggested. In this model, overall games were designed with the focus on the integration of curriculum and content subjects related to learning activities. For games not to be biased toward entertainment and to have systemized learning steps, the games are composed of an introduction, presentation, practice, production and evaluation, in that order. The model suggested by this plan and composition make it possible to approach learning efficiently with entertaining games based on a systematic learning curriculum. As shown above, developing the model of educational computer games can be seen as an opportunity, which can provide amusement and interests and a broad learning experience as an additional learning method.

• Journal of Agricultural Extension & Community Development
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• v.22 no.4
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• pp.435-444
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• 2015

This study analyzes the structural relationship between attitudes and learning more properties in agricultural college education programs. The results were as follows. first, The model was accepted according to the some goodness of fit statistics such as ${\chi}^2$(84.28, p>.05), RMR(.036), RMSEA(.041), GFI(.927), NFI(.945), CFI(.985), IFI(985). seconds, Learning attitude(.31) and content validity(.47) in the structural relationship between variables is a direct impact on satisfaction. thirds, Perceived Usefulness(.34) and Content validity(.36) has direct effect of factor on learning attitude. Finally Perceived Usefulness was found to direct effect all Content validity(.64) and easy of use(.27). Finally, considering of duties required in the agriculture. increase the satisfaction of learners should have provide field learning based Learning materials, practices, instructional media. As a result, it will enhance the performance of field learning agricultural education programs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.700-703
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• 2003

In recent years, amount of studies on application of wetbased educational system are proceeding in educational field. This study proposes design and implementation for web-based learning system using WMT in order to practise assistance learning between teacher and students on the web. For system design and implementation, this study utilizes Windows 2000 Server and Windows Media Technology. Using WMT, we make teaming materials and put them in web-based educational system. It should be possible to make teaming in accordance with Windows Media Player for a long-distance students. As a result, this study expects to accomplish a web-based learning between teacher and student on the web.

• Journal of Korean society of Dental Hygiene
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• v.18 no.5
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• pp.729-740
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• 2018

Objectives: The purpose of this study was to investigate the factors affecting robot utilization in the education of pre-dental hygienists. Methods: A self-reported questionnaire was completed by 238 dental hygiene students studying in the Daejeon, Chungcheong, and Jeolla provinces during the period March 1-31, 2017. Results: Future oral health education media had high selection of 'movies,' 'video,' '3D printer,' 'robot,' and 'drone' In general education and oral health education, robots were appropriate as educators, assistant teachers, and media. This group had high levels of interest, experience, attitude, and learning scope of robots. Robot utilization education showed a significant positive correlation with the 'interest,' 'experience,' 'attitude,' and 'learning' subfactors (p<0.01). Factors influencing robot utilization education were the relationships among actual experience of robot, learning of robot production, social influence of robot, emotional exchange with robot, and the predictive power was 25.5% (p<0.05). Conclusions: Oral health education curricula using robots should be developed considering the emotional exchange and social influence between educator and learner.

• Journal of Korea Game Society
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• v.15 no.3
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• pp.189-200
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• 2015

These days computer graphics technology has been applied in diverse IT fields. Needs for computer graphics such as 3D Printer, Head Mount Display, VR & AR are growing rapidly. Computer graphics will be more specialized and demanding for graphics specialists will be also increased. However, serious mathematical background obstructs people to learning computer graphics. An efficient computer graphics learning system would be helpful for graphics experts training. By analyzing the graphics theory, we propose an educational software system with that students can effectively learn computer graphics. Our system focuses on theoretical objects of computer graphics and enhances accessibility and intuition using web and blocks.

• Smart Media Journal
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• v.7 no.3
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• pp.16-21
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• 2018

This paper presents the design and implementation of a deep learning-based automated weed detector on onion fields. The system is based on a Convolutional Neural Network that specifically selects proposed regions. The detector initiates training with a dataset taken from agricultural onion fields, after which candidate regions with very high probability of suspicion are considered weeds. Non-maximum suppression helps preserving the less overlapped bounding boxes. The dataset collected from different onion farms is evaluated with the proposed classifier. Classification accuracy is about 99% for the dataset, indicating the proposed method's superior performance with regard to weed detection on the onion fields.