• Journal of The Korean Association of Information Education
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• v.17 no.3
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• pp.291-304
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• 2013

Considering the change of educational environments and strategies for the future, this research attempted to develop project learning that uses various smart technologies, and integrates formal education within a school with informal learning experiences outside of the school. For effective learning, the processes of the project learning, instructional activities for each process and supporting materials were specified and developed as a learning package. The project learning program and the instructional package were applied to 18 fifth graders in an elementary school located in Seoul. The results of the pilot test were collected with observations, interviews, and assessment of learning processes and products. And then the results were analyzed in regard of 'the whole processes of project activities', 'learning materials and tools', and 'informal learning experiences'. Based on the results, some suggestions were provided for implementing the smart project learning for integrative learning experiences.

• Journal of Korean Elementary Science Education
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• v.40 no.2
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• pp.127-144
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• 2021

The purpose of this research was to conduct grounded-theory-based explorations on the types of motivation that make pre-service elementary school teachers learn science and on their type-based science-learning processes. One hundred thirty-two pre-service elementary school teachers' motivation types were analyzed, and amongst them, 12 were selected as the subjects to observe their science-learning processes to which grounded theory applied. As a result of analyzing their science-learning motivation types, it was found that the majority belonged to the type "accurators", followed in descending order by the types "directors", "explorers", and "coordinators". Coding various phenomena that appeared in their science-learning processes made it possible to derive 30 categories from them according to the grounded-theory paradigm model elements. Based on such categories derived, analysis could be made on their science-learning process flows by motivation types, according to the grounded-theory paradigm model. For example, the "accurators" were attending science lectures or reading science books to learn science knowledge and how to teach it, from a sense of obligation they took for granted as elementary school teachers. Although their experiences of science-learning processes could not be from pure intentions, due to the teacher certification examination, curriculum, or other environmental factors, they were found to have new perspectives on science with their individual efforts and participations.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.375-380
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• 2019

In this paper, we study learning process mining and analytic technology based on open education platform. A study on mining through personal learning history log data based on an open education platform such as MOOC which is growing in interest recently. This technology is to design and implement a learning process mining framework for discovering and analyzing meaningful learning processes and knowledge from learning history log data. Learning process mining framework technology is a technique for expressing, extracting, analyzing and visualizing the learning process to provide learners with improved learning processes and educational services.

• Educational Technology International
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• v.10 no.1
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• pp.1-23
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• 2009

Learners are often posited in a paradoxical situation where they are not fully involved in decision making processes on how to learn, in designing their tools. Cognitive artifacts in e-learning are supposed to effectively support learner-centered e-learning. The purpose of the study is to analyze cases of cognitive artifacts and to inquire those design principles for facilitating the learner-centered e-learning. Four research questions are suggested: First, it will be analyzed the characteristics of learners with respect to design of cognitive artifacts for supporting the learner-centered e-learning. Second, characteristics of four cases to design cognitive artifacts in learner-centered e-learning environment are analyzed. Third, it will be suggested the appropriate design principles of cognitive artifacts to facilitating learner-centered learning in e-learning environment. Four cases of cognitive artifacts design in learner-centered e-learning was identified as follows: Wiki software as cognitive artifacts in computer-supported collaborative learning; 'Play Around Network (PAN)' as cognitive artifact to monitor learning activities in knowledge community; Knowledge Forum System (KFS) as a cognitive artifact in knowledge building; cognitive artifacts in Courses-as-seeds applied meta-design. Five design principles are concluded as follows: Promoting externalization of cognitive artifacts to private media; Helping learners to initiate their learning processes; Encouraging learners to make connections with other learners' knowledge building and their cognitive artifacts; Promoting monitoring of participants' contributions in collaborative knowledge building; Supporting learners to design their cognitive artifacts.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.127-135
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• 2013

For the successful implementation of IT projects, individual consultant's competency in the project is very important. Especially, 3 key factors which are 1) Learning-by-Doing, 2) Learning-from-Others, and 3) Learning-by-Investment with individual consultant's competency, are required for solving various critical issues which can be occurred during implementing IT project. The objective of this research is to examine the effects of these learning processes on decision performance of consultants. Prior to setup the research model, we conducted 3 times in-depth interviews with IT consultants who have over 20 years IT project experiences. Through interviews with IT project expert, we tried to validate our research model and develop survey questionnaires. Over 100 consultants, who are working at SI companies those of Samsung SDS, LG CNS, SK C&C and other small SI companies, were participated to survey. In the contrary of our thoughts before conducted experiment, we got the interesting result from pilot experiment. Most influenced learning process was Learning-by-Doing and less influenced learning process was Learning-from-Others.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of Industrial Convergence
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• v.7 no.2
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• pp.33-44
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• 2009

I first present a set of features for distinguishing group learning from other concepts. I then develop a framework for understanding group learning that focuses on learning's basic processes at the group level of analysis: storage, and retrieval.

• Journal of Industrial Convergence
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• v.7 no.2
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• pp.45-57
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• 2009

I first present a set of features for distinguishing group learning from other concepts. I then develop a framework for understanding group learning that focuses on learning's basic processes at the group level of analysis: sharing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.44-46
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• 2021

Many cameras are used in real life, and they are often used for monitoring and crime prevention to check the situation of problems beyond just taking pictures for memories. Such surveillance and prevention are generally used only for simple storage, and in systems utilizing multiple cameras, utilizing additional features would require additional hardware specifications. In this paper, we add image input methods and post-object processing processes to process multiple image inputs from one hardware or server that perform object detection systems that deviate from typical image processing. The performance of the method is utilized in both learning and reasoning of the hardware performing deep learning, and allows improved image processing processes to be performed.