• Educational Technology International
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• v.9 no.1
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• pp.79-96
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• 2008

The issue of connection between entities has a lengthy history in educational research, especially since it provides the necessary bridge between base and target in analogical transfer. Recently, the connection has been viewed through the application of technology to bridge between sequences in order to be cognitively useful. This study reports the effect of sequence type (AT vs. TA) and connection type (fading vs. popping) on the achievement and analogical transfer in a multimedia application. In the current research, 10th -grade and 11th -grade biology students in Korea were randomly assigned to five groups to test the effects of presentation sequence and entity connection type on analogical transfer. Consistent with previous studies, sequence type has a significant effect: analogical transfer performance was better when base representations were presented first followed by target representations rather than the reverse order. This is probably because presenting a familiar base first helps in understanding a less familiar target. However, no fully significant differences were found with the entity connection types (fading vs. popping) in analogical transfer. According to the Markman and Gentner's (2005) spatial model, analogy in a space is influenced only by the differences between concepts, not by distance in space. Thus connection types fail on the basis of this spatial model in analogical transfer test. The findings and their implications for sequence and connection research and practice are discussed. Leveraging on the analogical learning process, specific implications for scaffolding learning processes and the development of adaptive expertise are drawn.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1387-1395
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• 2018

Core algorithm of deep learning Convolutional Neural Network(CNN) shows better performance than other machine learning algorithms. However, if there is not sufficient data, CNN can not achieve satisfactory performance even if the classifier is excellent. In this situation, it has been proven that the use of transfer learning can have a great effect. In this paper, we apply two transition learning methods(freezing, retraining) to three CNN models(ResNet-50, Inception-V3, DenseNet-121) and compare and analyze how the classification performance of CNN changes according to the methods. As a result of statistical significance test using various evaluation indicators, ResNet-50, Inception-V3, and DenseNet-121 differed by 1.18 times, 1.09 times, and 1.17 times, respectively. Based on this, we concluded that the retraining method may be more effective than the freezing method in case of transition learning in image classification problem.

• Asia pacific journal of information systems
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• v.16 no.2
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• pp.85-110
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• 2006

The objective of this research is to identify the process of knowledge transfer and to examine the effect of IT human capability on knowledge transfer in information systems outsourcing. Through a field survey, it is found that clients' IT human capability significantly affects on cooperative learning with vendors and knowledge transfer from vendors to clients. The survey also shows that clients' trust with vendors indirectly enhances the knowledge transfer by increasing the cooperative learning. This study provides a solution of knowledge transfer problem in information systems outsourcing. Also it brings out issues that can be accrued in the outsourcing situation, such as clients' dependency on vendors and knowledge asymmetries developed in favor of the vendors. These issues should be topics for future research on information systems outsourcing.

• Journal of Engineering Education Research
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• v.12 no.3
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• pp.3-12
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• 2009

The recent development of engineering design education has brought enormous influence in many engineering educations. But, most studies in this area have focused only on the system or curriculum development rather than on the effect of the program to the real situation, the transfer. The purpose of this study is to identify the effects of learning and learning transfer in engineering design program at the university level. Transfer is defined as the use of trained knowledge and skill back on the job. The results of the study are as follows. First, learner characteristics and curriculum design had a significant influence on learning effectiveness. Second, learner characteristics had a significant influence an learning transfer. Also, the learning had a significant influence an learning transfer.

• Korean Security Journal
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• no.16
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• pp.243-264
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• 2008

Investigation about training of Private Security in Korea has managed status of training and understanding of issue mostly, systematic analysis about transfer effect as well as learning and determinant of training is insufficient. Consequently this investigation research to examine the effect of training program for newly appointed private security guards to learning and transfer then, to analyze factor through incumbent private security guard-oriented hypothesis testing under article 13, section 1 of the security guard law. To grasp effect factors of learning and transfer, this dissertation establishes research model and research hypothesis through domestic and foreign studies and then theoretical as well as positive literatures consideration in the first place. Conclusion of this investigation through techniques mentioned above and data analysis is as follows. First of all, the personal characteristics, training characteristics, work environment, difference of transfer as well as learning are verified in accordance with Sociodemographic characterist of Private Security Guard. After then, there is a difference of great import. Secondly, the effect of personal characteristics of Private Security Guard to the transfer is inspected. As a result, there is a difference of significance statistical. Thirdly, the effect of training characteristics of Private Security Guard to the transfer is inspected. Consequently, there is a difference of import statistical. The following thing, subsequent to inspection about the effect of work environment of Private Security Guard to transfer, statistical difference of import is.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.420-437
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• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• Journal of Industrial Convergence
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• v.20 no.5
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• pp.29-38
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• 2022

Because of the need of actual performance of education, this study aims to understand how the factors of educational content and organizational characteristics affect organizational commitment and work performance, which are organizational effects, through learning transfer. As a result, task value, job relevance, and organizational compensation had a significant effect on learning transfer, learning transfer had a significant effect on organizational commitment and work performance, and organizational commitment had a significant effect on work performance. In order to increase the learning transfer of education, when specifying the connection with the actual job and strengthening the compensation system of the members, the learning transfer can be increased and eventually connected to performance. Since limited variables are considered, a more representative sample or professional group should be extracted through future research. In future studies, it will be possible to closely grasp the relationship between learning transfer and organizational effectiveness by setting representative samples and specifying variables.

• Fire Science and Engineering
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• v.30 no.3
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• pp.117-123
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• 2016

To accomplish the purpose, the current study drew factors affecting the transfer of education and training through a review of domestic and overseas literature, and aimed to empirically investigate whether these factors actually affect the transfer of education and training of fire officers. The results showed that significant variables affecting the degree of perception on the transfer of education and training were in the order of work relationship, learning culture, peer support, self-efficacy, learning motivation, learning ability, and teaching method.

• Journal of the Korean Home Economics Association
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• v.26 no.2
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• pp.69-79
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• 1988

This study was to increase the experimental learning effect of textile materials in middle and high school. To this study, three kinds of simply devised apparatus were used for warmth retaining test, air permeability test and static electricity test. Two classes were chosen in a girls' high school comparing learning effect and classified the theoretical learning group and the experimental learning group. In the experimental group, a teaching plan to teach the properties of textile materials was made to use these apparatuses. The results were. 1. In the interest on the unit either the theoretical learning group or the experimental learning group were not different significantly. 2. In the items facilitation of motivation, unsatisfied desires and prevention of failure, effect of transfer and development of inquiry power, both groups were significant. 3. For the purpose of comparing the learning effect, two groups were examined for determining the level of understanding after teaching properties of textile materials. The mean value of the experimental learning group was higher than that of the theoretical learning group. The experimental learning group had more higher markers(over the point of 90) than the theoretical learning group.