• Journal of Korean Biological Nursing Science
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• v.17 no.1
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• pp.78-87
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• 2015

Purpose: The purpose of this study was to investigate the effects of case-based learning (CBL) on learning motivation and learning satisfaction of nursing students in a human physiology course. Methods: The development and application of CBL scenarios was conducted from February to June, 2013. Nursing students (n=142) who registered for a human physiology course were assigned into either a control or CLB group. The control group received traditional lectures for 14 weeks. The CBL group received the same 14-week lectures and an additional 5 CBL sessions. The learning motivation and satisfaction were measured by questionnaires at the beginning and the end of the semester. Seven students in the CBL group were randomly selected for a focus-group interview. Quantitative data were analyzed by ${\chi}^2$-test and t-test, and qualitative data were analyzed by content analysis. Results: The learning motivation and learning satisfaction were not significantly different between the two groups. However, 59% of the CBL group answered with a positive impression on the CBL approach as it helped them to feel a sense of achievement, excitement, to form their identity as nursing students, and so on. Conclusion: These findings suggest that the CBL could be a challenging but useful learning method in a physiology course for nursing students. Further studies with guidance, such as instructors' questions and feedback design are needed to utilize CBL more effectively.

• New & Renewable Energy
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• v.5 no.3
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• pp.49-55
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• 2009

Human resource for the new and renewable energy technology is an important factor in the respect of the sustainable growth and energy security. In this paper, we focused on measuring the economic effect of human resource development on new and renewable energy development programs. The human capital accumulation model developed by Mincer (1974) was modified in terms of the rate of the researchers' investment in human capital. As a result of a empirical case study, the value of human capital was estimated by 102 million Korean won per year worth 18% of the project labor cost. In case of the assumption of 100% participation of researchers, the level of human capital accumulation increased to 914 million Korean won per year. These results imply that the new and renewable energy development programs has been successful, on the concept of learning by doing, in terms of providing the researchers with opportunities to accumulate human capital.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.409-420
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• 2012

The decision-making by agents in games is commonly based on reinforcement learning. To improve the quality of agents, it is necessary to solve the problems of the time and state space that are required for learning. Such problems can be solved by Macro-Actions, which are defined and executed by a sequence of primitive actions. In this line of research, the learning time is reduced by cutting down the number of policy decisions by agents. Macro-Actions were originally defined as combinations of the same primitive actions. Based on studies that showed the generation of Macro-Actions by learning, Macro-Actions are now thought to consist of diverse kinds of primitive actions. However an enormous amount of learning time and state space are required to generate Macro-Actions. To resolve these issues, we can apply insights from studies on the learning of tasks through Programming by Demonstration (PbD) to generate Macro-Actions that reduce the learning time and state space. In this paper, we propose a method to define and execute Macro-Actions. Macro-Actions are learned from a human subject via PbD and a policy is learned by reinforcement learning. In an experiment, the proposed method was applied to a car simulation to verify the scalability of the proposed method. Data was collected from the driving control of a human subject, and then the Macro-Actions that are required for running a car were generated. Furthermore, the policy that is necessary for driving on a track was learned. The acquisition of Macro-Actions by PbD reduced the driving time by about 16% compared to the case in which Macro-Actions were directly defined by a human subject. In addition, the learning time was also reduced by a faster convergence of the optimum policies.

• Korean Journal of Human Ecology
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• v.23 no.5
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• pp.759-771
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• 2014

The purpose of this study was to examine the effects of undesirable parenting behavior, children's peer relationship and self-regulated learning on children's self-esteem. Using the data from Korean Children and Youth Panel Survey, this study was conducted with Structural Equation Modeling(SEM). The results of this study were as follows. First, parents' undesirable parenting behavior influenced directly on children's self-esteem, and peer relationship. Second, children's peer relationship influenced directly on self-regulated learning, and self-esteem. Third, children's self-regulated learning influenced directly on self-esteem. Fourth, parents' undesirable parenting behavior did not influenced directly on children's self-regulated learning. But children's peer relationship and self-regulated learning had mediating effects on the relationship between undesirable parenting behavior and children's self-esteem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.120-123
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• 1997

In this paper, we propose the Fuzzy Inference-based Reinforcement Learning Algorithm. We offer more similar learning scheme to the psychological learning of the higher animal's including human, by using Fuzzy Inference in Reinforcement Learning. The proposed method follows the way linguistic and conceptional expression have an effect on human's behavior by reasoning reinforcement based on fuzzy rule. The intervals of fuzzy membership functions are found optimally by genetic algorithms. And using Recurrent state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying to the inverted pendulum control problem.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.619-624
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• 2014

This paper introduces a robot-based learning system which is designed to teach multiplication to children. In addition to a small humanoid and a smart device delivering educational content, we employ a type of mixed-initiative operation which provides enhanced multi-modal cognition to the r-learning system through human intervention. To investigate major factors that influence people's intention to use the r-learning system and to see how the multi-modality affects the connections, we performed a user study based on TAM (Technology Acceptance Model). The results support the fact that the quality of the system and the natural interaction are key factors for the r-learning system to be used, and they also reveal very interesting implications related to the human behaviors.

• Journal of the Korean Home Economics Association
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• v.36 no.5
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• pp.59-74
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• 1998

Home economics lesson plan model was developed based on cooperative learning stategy focusing on human development and family relations area. The cooperative learning is an instructional strategy that meets the challenge of helping students master home economics content objectives by acquiring and practicing the social skills that are essential in life for satisfactory relationships with peers, families, coworkers, and others in society. Through cooperative learning, students can satisfy their needs for friendship, power, belongs, and fun. Practical problems were selected in human development and family relations area for middle school students assuming that home economics is critical science. Lesson plans were developed according to the practical problems, "what should we do regarding the effective communication between family members\ulcorner".ner".uot;.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.293-299
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• 1995

A methodology is applied to identify tile learning trend related to the safety and availability of U.S. commercial nuclear power plants. The application is intended to aid in reducing likelihood of human errors. To assure that tile methodology ran be easily adapted to various types of classification schemes of operation data, a data bank classified by the Transient Analysis Classification and Evaluation(TRACE) scheme is selected for the methodology. The significance criteria for human-initiated events affecting tile systems and for events caused by human deficiencies were used. Clustering analysis was used to identify the learning trend in multi-dimensional histograms. A computer rode is developed based on tile K-Means algorithm and applied to find the learning period in which error rates are monotonously decreasing with plant age.

• Korean Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.111-121
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• 1997

This study intends to develop a fixture planning module as a part of the planning system for cutting. The fixture module uses machine learning method to reuse previous failure results so that the system can reduce the repeated failures. Machine learning is one of efforts to incorporate human reasoning ability into a computerized system. A human expert designs better than a novice does because he has a wide experience in a specific area. This study implements the machine learning algorithm to have a wide experience in the fixture planning area as a human expert does. When the fixture planner finds a setup failure for the suggested operations by a process planner, it makes the process planner store its attributes and other information for the failed setup. Then the process planner applies the learned knowledge when it meets a similar case so that the planner can reduce possibility of setup failure. Also the system can teach a novice user by showing a failed setup with a modified setup.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1210-1215
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• 2018

Recently, as the IoT(Internet of Things) and AI(Artificial Intelligence) technologies have developed, smart toys that can understand and act on the language of human beings are being studied. In this paper, we study voice learning using CNN(Convolutional Neural Network) by applying artificial intelligence based voice secretary technology to smart toy. When a human voice command gives, Smart Toy recognizes human voice, converts it into text, analyzes the morpheme, and conducts tagging and voice learning. As a result of test for the simulator program implemented using Python, no malfunction occurred in a single command. And satisfactory results were obtained within the selected simulation condition range.