• Journal of Fisheries and Marine Sciences Education
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• v.28 no.6
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• pp.1834-1847
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• 2016

Main objectives of the this study are: to develop a model of "Flipped Leaning" that is designed to enhance self-directed learning, learning motivation and self-control, and to verify its effectiveness-in higher education. The verification process initially concentrated on the feasibility study of the model with a thorough literature review and case analyses; then, its general and practical applicability were tested with a field study. As a result, first, the CHANGE Class Model, specifically designed for effective and efficient "Flipped Learning", was developed. It is thus named for the stages that the learning process takes place in the model-i.e., (1) Check ${\rightarrow}$ (2) Ask ${\rightarrow}$ (3) Notice ${\rightarrow}$ (4) Group presentation ${\rightarrow}$ (5) Evaluation, and it emphasizes the dynamic, questions centered (i.e. back and forth between the students and the instructor as well as between the students) learning process. Second, the Model was instrumental in enhancing self-directed learning, learning motivation and self-control; thus, as a result, it significantly improved the effectiveness, the level of concentration and the attractiveness of the learning process. The value of this study lies in pointing to a clear plan to allow a student in higher learning to set-up a self-directed learning plan, to be able to control it while being continuously motivated to complete it.

• Journal of The Korean Association For Science Education
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• v.19 no.2
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• pp.305-314
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• 1999

The effects of three teaching models were compared in this research. One of those is concept change model, another is concept change model based on students learning motivations, the other is traditional teaching method based on science textbooks. The subjects of this research were the 8th grade students of Korean middle school. They were divided into three groups, and tested learning motivations. All of the three groups improved their learning motivations and concept understanding by the classes. Especially, the group of concept change model based on students learning motivations represented most effective improvement of learning motivations. The concept change teaching model and concept change teaching model based on students learning motivations are more effective in concept understanding than traditional teaching method based on textbooks. The students who have high learning motivations improved their concept understanding by the classes of concept change model based on students learning motivations. The students who have low learning motivations improved their learning motivations by the classes of concept change model based on students learning motivations also.

• Research in Mathematical Education
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• v.27 no.1
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• pp.129-150
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• 2024

This study utilized South Korean elementary and middle school student data to examine the longitudinal change trajectories of learning motivation types according to the longitudinal change trajectories of mathematics academic achievement. Growth mixture modeling, latent growth model, and multiple indicator latent growth model were used to examine various change trajectories for longitudinal data. As a result of the analysis, it was classified into 4 subgroups with similar longitudinal change trajectories of mathematics academic achievement, and the characteristics of the mathematics subject, which emphasize systematicity, appeared. Furthermore, higher mathematics academic achievement was associated with higher self-determination and higher academic motivation. And as the grade level increases, amotivation increases and self-determination decreases. This study suggests that teaching and learning support using this is necessary because the level of learning motivation according to self-determination is different depending on the level of mathematics academic achievement reflecting the characteristics of the student.

• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.59-82
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• 2023

The present research aims to examine whether learning from the supervisor influences readiness for change with the mediating impact of planfulness. Drawing upon the theory of planned behavior, it is hypothesized that learning from the supervisor positively impacts planfulness ability in individuals, which in turn enhances the readiness for change. Through using convenience sampling, the sample of 451 was collected from employees working full-time in the manufacturing and I.T. service organizations in India. Structural equation modeling and regression analysis indicate that learning from the supervisor is positively associated with readiness for change and planfulness. Additionally, planfulness fully mediated the relationship between learning from the supervisor and readiness to change. The findings of the present research highlight that continuous support and learning from the supervisor enhances the planfulness ability of the individual and consequently enhances individual readiness for change. The current research is pioneering in testing the hypothetical model associating learning from the supervisor, planfulness, and readiness for change.

• Journal of The Korean Association For Science Education
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• v.19 no.1
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• pp.91-99
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• 1999

The researches related to students' preconceptions and conceptual change model have been reported that students' learning motivation is one of the key variable for the conceptual change. The effects of students learning motivations on conceptual changes were evaluated. Subjects of this study were 8th grade students. and they were divided into 2 groups. One group was taught by traditional teaching method, and the other group by concept change teaching model. After the intervention, learning motivations of the students were testified. The students of high motivation who were taught by concept change teaching model showed higher scores in the concept of chemical change than the students by traditional teaching method. But there was no difference in both groups of students who have low learning motivations. The learning motivations before the intervention. the motivations stimulated by classes. and the degree of concept understanding showed high correlation. The motivations stimulated by classes explain 23.3 % of the degree of concept understanding. The results seems to mean that students learning motivations contribute to the understanding of concepts. Especially confidence of learning as a subcategory of the learning motivation contributes significantly to the understanding of new concepts. In contrast, the traditional teaching methods and the teaching methods of concept change learning theory were not effective for the stimulation of students learning motivations.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.929-937
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• 2020

In this manuscript, the UNet++ model, which is one of the representative deep learning techniques for semantic segmentation, was used to detect changes in temporal satellite images. To analyze the learning results according to various loss functions, we evaluated the change detection results using trained UNet++ models by binary cross entropy and the Jaccard coefficient. In addition, the learning results of the deep learning model were analyzed compared to existing pixel-based change detection algorithms by using WorldView-3 images. In the experiment, it was confirmed that the performance of the deep learning model could be determined depending on the characteristics of the loss function, but it showed better results compared to the existing techniques.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1685-1695
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• 2021

To generate a deep learning model with high performance, a large training dataset should be required. However, it requires a lot of time and cost to generate a large training dataset in remote sensing. Therefore, the importance of transfer learning of deep learning model using a small dataset have been increased. In this paper, we performed transfer learning of trained model based on open datasets by using orthoimages and digital maps to detect changes of building objects in multitemporal orthoimages. For this, an initial training was performed on open dataset for change detection through the HRNet-v2 model, and transfer learning was performed on dataset by orthoimages and digital maps. To analyze the effect of transfer learning, change detection results of various deep learning models including deep learning model by transfer learning were evaluated at two test sites. In the experiments, results by transfer learning represented best accuracy, compared to those by other deep learning models. Therefore, it was confirmed that the problem of insufficient training dataset could be solved by using transfer learning, and the change detection algorithm could be effectively applied to various remote sensed imagery.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.233-244
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• 2023

Although artificial intelligence and machine learning technologies have been used in various fields, problems with personal information protection have arisen based on centralized data collection and processing. Federated learning has been proposed to solve this problem. Federated learning is a process in which clients who own data in a distributed data environment learn a model using their own data and collectively create an artificial intelligence model by centrally collecting learning results. Unlike the centralized method, Federated learning has the advantage of not having to send the client's data to the central server. In this paper, we quantitatively present the performance improvement when federated learning is applied using the building change detection learning data. As a result, it has been confirmed that the performance when federated learning was applied was about 29% higher on average than the performance when it was not applied. As a future work, we plan to propose a method that can effectively reduce the number of federated learning rounds to improve the convergence time of federated learning.

• The Mathematical Education
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• v.50 no.1
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• pp.103-118
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• 2011

The study examined whether the relation between mathematics self-efficacy and mathematics achievement was partially mediated by the learning strategies, using latent growth model analyses. It was also examined the auto-regressive, cross-lagged (ARCL) panel model for testing the stability and change in the relation of mathematics self-efficacy and learning strategy over time. The study analyzed the first-year to the third-year data of the Korean Educational Longitudinal Survey (KELS). The result of ARCL panel model analysis showed that earlier mathematics self-efficacy could predict later learning strategy use. There were linear trends in mathematics self-efficacy, learning strategy, and mathematics achievement. Specifically, mathematics achievement was increased over the three time points, whereas mathematics self-efficacy and learning strategies were significantly decreased. In the analyses of latent growth models, the mediating effects of learning strategies were overall supported. That is, both of initial status and change rate of rehearsal strategy partially mediated the relation of mathematics self-efficacy and mathematics achievement. However, in elaboration and meta-cognitive strategies, only the initial status of each variable showed the indirect relationship.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.