• Journal of The Korean Association of Information Education
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• v.4 no.2
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• pp.187-201
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• 2001

A project learning is the model to solve the problem in cooperation with the members of a group. The purpose of this study is to make a new learning model using a web on the traditional project learning model. A Web has the proper nature for students to make their learning environment, so that this study embodied Student Directed Web based project learning system to allow them to expand their abilities. All action of students, that is, suggesting main subject, making plans, activating and adjusting them, and evaluation can be happened in this system. According to the result of applying the system, participants in Students Directed Web based Project Learning intend to implement learning activities more positively, affirmatively and cooperatively than the existing ones.

• The Journal of Information Technology
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• v.5 no.2
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• pp.1-9
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• 2002

This papers is a study on design and implementation of a web-based courseware for learning Yungwol local community through the internet which is one of the self leading learning methods. Learners can learn Yungwol local community by using the web-based courseware and on the basis of this learning methods, learners can use them according to their wish at anytime and anyplace. The learner-centered teaching model can be offered by sharing, exchanging, and interacting the information each other. To achieve such purpose, two learning grounds are applied. One is the research learning ground which students can study for themselves in the course of searching for the changing cause which can affect to local community and culture. And the other is the experimental ground where is the changing cause searched from research learning ground. Social and physical environment of the lot of community can realized by surfing the network and e-mails producing web-based file through internet. If these web-based research community is more developed and used under the local area network school, self-leading research learning methods will be done regardless of the place and the time.

• Journal of Gifted/Talented Education
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• v.19 no.3
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• pp.457-476
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• 2009

The research aimed to investigate characteristics of middle school students in a biology class as science gifted education in terms of self-regulated learning abilities, personality traits and learning preferences. The twenty subject in the study responded to questionnaires of a self-regulated learning ability instrument, a personality trait tool, and a learning preference survey in March, 2009. It was found that the research subjects showed higher levels of cognitive strategies, meta-cognition, and motivation than those students in a previous study(Jung et. al., 2004), while environment was opposite. The level of cognitive strategies was significantly correlated with meta-cognition(r=.610, p=.004) and motivation (r=.538, p=.014) and meta-cognition with environment(r=.717, p=.000). Those students who showed highest levels of self-regulated learning ability displayed various personality traits. One male student with the highest level of self-regulated learning ability showed a personality of hardworking, tender-minded, and conscientious traits and wanted to be a medical doctor. The female student with the second highest level of self-regulated learning ability presented a personality as creative, abstract and divergent thinker and she showed a strong aspiration to be a world-famous biologist with breakthrough contribution. The five students with highest levels of self-regulated learning ability showed a common preference in science learning: they dislike memory-oriented and theory-centered lecture with note-taking from teacher's writings on chalkboard; they prefer science learning with inquiry-oriented laboratory work, discussion among students as well as teachers. However, reasons to prefer discussion were diverse as one student wants to listen other students' opinions while the other student want to present his opinion to other students. The most favorable science teachers appeared to be who ask questions frequently, increase student interests, behave friendly with students, and is a active person. In conclusion, science teaching for the gifted should employ individualized teaching strategies appropriate for individual personality and preferred learning styles as well as meeting with individual interests in science themes.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.35-41
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• 2018

The Q-learning algorithm based on reinforcement learning is useful for learning the goal for one behavior at a time, using a combination of discrete states and actions. In order to learn multiple actions, applying a behavior-based architecture and using an appropriate behavior adjustment method can make a robot perform fast and reliable actions. Q-learning is a popular reinforcement learning method, and is used much for robot learning for its characteristics which are simple, convergent and little affected by the training environment (off-policy). In this paper, Q-learning algorithm is applied to a lamp robot to learn multiple behaviors (human recognition, desk object recognition). As the learning rate of Q-learning may affect the performance of the robot at the learning stage of multiple behaviors, we present the optimal multiple behaviors learning model by changing learning rate.

• Journal of Intelligence and Information Systems
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• v.14 no.3
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• pp.59-76
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• 2008

In this paper, we propose an agent architecture called L-CAA that is quite effective in real-time dynamic environments. L-CAA is an extension of CAA, the behavior-based agent architecture which was also developed by our research group. In order to improve adaptability to the changing environment, it is extended by adding reinforcement learning capability. To obtain stable performance, however, behavior selection and execution in the L-CAA architecture do not entirely rely on learning. In L-CAA, learning is utilized merely as a complimentary means for behavior selection and execution. Behavior selection mechanism in this architecture consists of two phases. In the first phase, the behaviors are extracted from the behavior library by checking the user-defined applicable conditions and utility of each behavior. If multiple behaviors are extracted in the first phase, the single behavior is selected to execute in the help of reinforcement learning in the second phase. That is, the behavior with the highest expected reward is selected by comparing Q values of individual behaviors updated through reinforcement learning. L-CAA can monitor the maintainable conditions of the executing behavior and stop immediately the behavior when some of the conditions fail due to dynamic change of the environment. Additionally, L-CAA can suspend and then resume the current behavior whenever it encounters a higher utility behavior. In order to analyze effectiveness of the L-CAA architecture, we implement an L-CAA-enabled agent autonomously playing in an Unreal Tournament game that is a well-known dynamic virtual environment, and then conduct several experiments using it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5095-5111
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• 2019

A multi-rotor Unmanned Aerial Vehicle (UAV) system is developed to solve the manhole cover detection problem for the infrastructure maintenance in the suburbs of big city. The visible light sensor is employed to collect the ground image data and a series of image processing and machine learning methods are used to detect the manhole cover. First, the image enhancement technique is employed to improve the imaging effect of visible light camera. An imaging environment perception method is used to increase the computation robustness: the blind Image Quality Evaluation Metrics (IQEMs) are used to percept the imaging environment and select the images which have a high imaging definition for the following computation. Because of its excellent processing effect the adaptive Multiple Scale Retinex (MSR) is used to enhance the imaging quality. Second, the Single Shot multi-box Detector (SSD) method is utilized to identify the manhole cover for its stable processing effect. Third, the spatial coordinate of manhole cover is also estimated from the ground image. The practical applications have verified the outdoor environment adaptability of proposed algorithm and the target detection correctness of proposed system. The detection accuracy can reach 99% and the positioning accuracy is about 0.7 meters.

• Korean Medical Education Review
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• v.25 no.3
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• pp.229-242
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• 2023

Health systems science is a new medical educational field added to the traditional medical education curricula of basic and clinical sciences. Health systems science emphasizes a more comprehensive approach utilizing systems thinking to care for patients, including interactions between multiple healthcare systems. In this review, I explore how health systems science education can be applied when medical instructors teach students in clinical clerkships through representative case studies. This study first looks at examples of health systems science education in clinical clerkship in the United States and suggests how to develop the curriculum of health systems science for clinical learning environments in Korea by combining Kotter's 8-step change management model and Kern's 6-step curriculum development model. Finally, based on practical examples from actual clinical practice education situations, suggestions are made regarding how to develop the entire educational program of a medical school from the stage of applying health systems science at the individual level to clinical practice education.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.85-93
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• 2012

This paper is to explore the effects of Robot Based Instruction(RBI) on the learning attitude of elementary school students. According to this research, researcher found out that there is significant improvement in learning attitude score after RBI was applied. The result of verification on the learning attitude is difference by sex showed that male students' learning attitude score is more high better than another group. In particular, it showed that there is more significant improvement in science art discretionary activities subjects. The above-mentioned results are based on as follows two reasons. First, RBI is efficient to improve students' internal motivation and ownership about tasks, and that is related to environment of learning and instruction focused on authentic task and practice. Second, educational advantages of robot media was reflected appropriately in RBI, also appropriate instructional environment for RBI was supported.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.205-222
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• 2014

This study analyzed the effects of interactivity on the learning attitude in the tablet PC-based digital textbook environment. Most of digital textbook studies focused on comparison of learning effect between digital textbook and paper textbook. This study, instead, focused on the interaction between students and digital textbook, and examined the hypothesis that, in the digital textbook-based learning environment, interactivity factors affect learning attitude. The results showed that active control, two-way communication, and synchronicity have significant effects on the learning attitude. Those findings indicate that it's necessary to effectively realize interactivity in the process of developing digital textbooks. Also, important implication is not the fixed interactivity but how students perceive the digital textbook and make use of it. Therefore, for the interactive digital textbook, perceived user control, two-way communication, and synchronicity should be realized properly.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.348-355
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• 2021

Asphalt, widely used for road pavement, has different required physical properties depending on the environment to which the road is exposed. Therefore, it is essential to maximize the life of asphalt roads by evaluating the physical properties of asphalt according to additives and selecting an appropriate formulation considering road traffic and climatic environment. Dynamic shear rheometer(DSR) test is mainly used to measure resistance to rutting among various physical properties of asphalt. However, the DSR test has limitations in that the results are different depending on the experimental setting and can only be measured within a specific temperature range. Therefore, in this study, to overcome the limitations of the DSR test, the rheological characteristics were predicted by learning the images collected from atomic force microscopy. Images and rheology properties were trained through EfficientNet, one of the deep learning architectures, and transfer learning was used to overcome the limitation of the deep learning model, which require many data. The trained model predicted the rheological properties of the asphalt binder with high accuracy even though different types of additives were used. In particular, it was possible to train faster than when transfer learning was not used.