• Journal of Korean Society for Quality Management
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• v.45 no.4
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• pp.847-866
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• 2017

Purpose: The purpose of this study is to examine the effects of educational service quality, participation intention, and educational performance in action learning class. Methods: The proposed research model is tested using structural equation modeling for hypotheses based on the data collected from one of action learning class. Results: The results indicate that educational service quality(reliability, assurance, tangibles, empathy, information accuracy, and relationship quality) positively affects participation intention which in turn improve educational performance, including aspects of before and after class of action learning. In addition, participation intention in classroom positively affects educational performance with both groups. For after class of action learning, the result confirms the effect of responsiveness of educational service quality on participation intention, however, in before class of action learning there is not showed a significant relationship. Conclusion: This study would provide useful information and can be applied to the improvement of educational performance through the participation of students by the instructors and the educational institutes who want to apply the active learning forum in classroom.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1147-1148
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• 2008

In this paper, we propose action-learning method based on teaching. By adopting this method, we can handle an exception case which cannot be handled in an Ethology-based Action SElection mechanism. Our proposed method is verified by employing AIBO robot as well as EASE platform.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.119-125
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• 2008

This paper presents the polygon-based Q-leaning and Cascade Support Vector Machine algorithm for object search with multiple robots. We organized an experimental environment with ten mobile robots, twenty five obstacles, and an object, and then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning and dodecagon-based Q-learning and Cascade SVM to enhance the fusion model with DBAM and ABAM process.

• The Journal of the Korea Contents Association
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• v.16 no.1
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• pp.285-294
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• 2016

The purpose of this study was to investigate the effects of the action learning-based spiritual care education program on nursing students' spiritual needs, spiritual well-being, and spiritual care competence. This study was a pre-post test design with single group and participants were recruited from second-year nursing students at a University. From September to December 2014, the students participated in the action learning-based spiritual care education program, which were held 16 times, had decreased their spiritual needs and improved spiritual well-being and spiritual care competence. The data were analyzed using paired t-test with the SPSS WIN 23.0 statistics program. The results of this study indicate that the action learning-based spiritual care education program was effective in decreasing spiritual needs and improving spiritual well-being and spiritual care competence for nursing students. The nursing students, which provide a holistic care, will grow up to be a professional nurse by learning the nursing process including spiritual care. Also, an action learning-based education program should be developed in the various fields.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.570-576
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• 2007

This paper presents a hexagon-based Q-leaning algorithm to find a hidden targer object with multiple robots. An experimental environment was designed with five small mobile robots, obstacles, and a target object. Robots went in search of a target object while navigating in a hallway where obstacles were strategically placed. This experiment employed two control algorithms: an area-based action making (ABAM) process to determine the next action of the robots and hexagon-based Q-learning to enhance the area-based action making process.

• Educational Technology International
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• v.11 no.1
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• pp.145-169
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• 2010

Many universities are searching for educational methods to cultivate problem-solving ability and cooperative learning ability or already trying to implement them. Problem Based Learning(PBL) and Action Learning(AL) are effective teaching and learning methods to cultivate men of talent qualified for problem-solving and cooperative learning abilities that universities are seeking after. PBL and AL have something in common in that learning is accomplished while learners are solving the authentic problem. But, in spite of this similarity, PBL and AL have differences. However, most literatures and cases on these two models introduce only the outline of commons and differences and do not provide teachers with actual helping aids to select a model appropriate for the actual design or operation of classes. Accordingly, many teachers usually select and utilize a familiar model rather than select a proper model to the nature of a subject and the educational goal. Teaching and learning methods or learning environment should be selected appropriately to the educational goal. This study indicates the characteristics of PBL and AL that are being introduced and utilized as a principal teaching and learning method of college education and then shows how this method can be realized in the university by comparing the cases of classes applied in two methods.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.6
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• pp.99-106
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• 2015

This is a case study on organizational learning with action learning. It targets B industry belonging to Shoe manufacturer. We apply action learning techniques as consulting skills to promote the organization of specific learning activities. Action Learning solves the challenges faced by the company with the ability to enhance the member while participating in the program. Therefore, it is a good methodology to overcome the uncertainty environment. Through a case study, in the maturing process of a learning organization can see the conditions that are necessary for the ongoing maintenance of that identity, organizational learning activities. Findings to the continued operation of the enterprise learning organization suggest the establishment of a learning organization, and direction and strategic importance. Systems and learning environments should be built and then repeat the process of practice to master the new learning organization. It suggests to learn a new organizations operating methods that require repetition of the course of action.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.792-812
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• 2022

Multi-agent systems often need to achieve the goal of learning more effectively for a task through coordination. Although the introduction of deep learning has addressed the state space problems, multi-agent learning remains infeasible because of the joint action spaces. Large-scale joint action spaces can be sparse according to implicit or explicit coordination structure, which can ensure reasonable coordination action through the coordination structure. In general, the multi-agent system is dynamic, which makes the relations among agents and the coordination structure are dynamic. Therefore, the explicit coordination structure can better represent the coordinative relationship among agents and achieve better coordination between agents. Inspired by the maximization of social group utility, we dynamically construct a factor graph as an explicit coordination structure to express the coordinative relationship according to the utility among agents and estimate the joint action values based on the local utility transfer among factor graphs. We present the application of such techniques in the scenario of multiple intelligent vehicle systems, where state space and action space are a problem and have too many interactions among agents. The results on the multiple intelligent vehicle systems demonstrate the efficiency and effectiveness of our proposed methods.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1049-1058
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• 2022

HAR(Human Action Recognition) such as anomaly and object detection has become a trend in research field(s) that focus on utilizing Artificial Intelligence (AI) methods to analyze patterns of human action in crime-ridden area(s), media services, and industrial facilities. Especially, in real-time system(s) using video streaming data, HAR has become a more important AI-based research field in application development and many different research fields using HAR have currently been developed and improved. In this paper, we propose and analyze a deep-learning-based HAR that provides more efficient scheme(s) using an intelligent AI models, such system can be applied to media services using RGB video streaming data usage without feature extraction pre-processing. For the method, we adopt Slowfast based on the Deep Neural Network(DNN) model under an open dataset(HMDB-51 or UCF101) for improvement in prediction accuracy.