• The Journal of Korean Association of Computer Education
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• v.19 no.6
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• pp.33-44
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• 2016

In this study, learning preferences of the informatics gifted participating in on-line programming practice has been investigated and the tutoring strategy based on learning styles was developed and verified its effectiveness. On-line programming learning is the environment in which learning is done through interaction with a tutor. For determination of the students' learning styles, Grasha-Reichmann's learning style was utilized. Key elements, learning activity and feedback, of tutoring strategy were derived based on learning style and developed on-line programming learning tutoring strategy. To verify the effectiveness, six-class period of learning C-language of 173 middle and high school students based on learning styles was applied for 12 weeks. As a result, the experiment group applied with a tutoring strategy based on the learning style showed significant improvements in terms of satisfaction and achievement than the control group.

• IE interfaces
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• v.18 no.4
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• pp.412-417
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• 2005

This paper describes the development of on-line Programmable Logic Controller(PLC) laboratories to teach the detailed operation of a PLC using distance learning approach. The PLC has become a key component to provide intelligence for machines in CIM environment. This study introduces a case study that teaches PLC programming in distance learning environment. The study describes the contents of PLC laboratories and workstation set ups. The study concludes with problems found and proposes the ways to improve the on-line laboratories.

• Journal of The Korean Association of Information Education
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• v.15 no.4
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• pp.603-612
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• 2011

This study suggested an educational curriculum(12 class periods) in order for higher level elementary school students to learn programming in discretionary activity hours using a line tracer and evaluated achievement level based on the outcome of learning by class period to assess the possibility of success. As a result, it could confirm that the programming learning using the line tracer was more excellent in creativity than the computer programming learning. In addition, it has been found that the programming learning method using the line tracer had a potential to be successful as a new creative tool that could replace the computer.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.310-315
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• 2005

This paper describes the development of on-line Programmable Logic Controller (PLC) laboratories to teach the detailed operation of a PLC using distance learning approach. The PLC has become a key component to provide intelligence for machines in CIM environment. This study introduces a case study that teaches PLC programming in distance learning environment. The study describes the contents of PLC laboratories and workstation set ups. The study concludes with problems found and proposes the ways to improve the on-line laboratories.

• Journal of The Korean Association of Information Education
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• v.15 no.4
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• pp.533-542
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• 2011

In this paper, we have selected traveling algorithms of Line-Tracer as the focused learning elements with the PBL-based programming instruction method. Line-Tracer traveling algorithm programming has been simulated using the Scratch EPL. Development of robot web courseware such as Line-Tracer can create an effective educational environment and also provide solutions for lack of environmental conditions, such as time or spatial factor restrictions and excessive expense issues; these are major obstacles to developing robot programming education. Finally, we analyzed the effects on growth of student's logical thinking and problem solving abilities by demonstrating the Scratch application courseware to the field of elementary education.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.452-455
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• 1999

We introduce an evolutionary approach to on-line learning for mobile robot control using reconfigurable hardware. We use genetic programming as an evolutionary engine. Control programs are encoded in tree structure. Genetic operators, such as node mutation, adapt the program trees based on a set of training cases. This paper discusses the advantages and constraints of the evolvable hardware approach to robot learning and describes a FPGA implementation of the presented genetic programming method.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.901-910
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• 2023

In universities, programming language-based thinking and software education for non-majors are being implemented to cultivate creative and convergent talent capable of leading the digital convergence era in line with the Fourth Industrial Revolution. However, learners face difficulties in acquiring the unfamiliar syntax and programming languages. The purpose of this study is to propose a software education model to alleviate the challenges faced by non-major students during the learning process. By introducing algorithm techniques and diagram techniques based on programming language thinking and using the EZMKER kit as an instructional model, this study aims to overcome the lack of learning about programming languages and syntax. Consequently, a structured software education model has been designed and implemented as a top-down system learning model.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3394-3408
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• 2014

Conventional mobile state (MS) and base station (BS) association based on average signal strength often results in imbalance of cell load which may require more powerful processor at BSs and degrades the perceived transmission rate of MSs. To deal with this problem, a Markov decision process (MDP) for load balancing in a multi-cell system with multi-carriers is formulated. To solve the problem, exploiting Sarsa algorithm of on-line learning type [12], ${\alpha}$-controllable load balancing algorithm is proposed. It is designed to control tradeoff between the cell load deviation of BSs and the perceived transmission rates of MSs. We also propose an ${\varepsilon}$-differential soft greedy policy for on-line learning which is proven to be asymptotically convergent to the optimal greedy policy under some condition. Simulation results verify that the ${\alpha}$-controllable load balancing algorithm controls the behavior of the algorithm depending on the choice of ${\alpha}$. It is shown to be very efficient in balancing cell loads of BSs with low ${\alpha}$.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.369-376
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• 2021

With the development of artificial intelligence (AI) technology, discussions on the use of artificial intelligence are actively taking place in many fields, and various policies for nurturing artificial intelligence talents are being promoted in the field of education. In this study, we propose a robot programming framework using artificial intelligence technology, and based on this, we use Python, which is used frequently in the machine learning field, and an educational robot that is highly utilized in the field of education to provide artificial intelligence. (AI) education program was proposed. The level of autonomous driving (levels 0-5) suggested by the International Society of Automotive Engineers (SAE) is simplified to four levels, and based on this, the camera attached to the robot recognizes and detects lines (objects). The goal was to make a line detector that can move by itself. The developed program is not a standardized form of solving a given problem by simply using a specific programming language, but has the experience of defining complex and unstructured problems in life autonomously and solving them based on artificial intelligence (AI) technology. It is meaningful.