• Journal of Engineering Education Research
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• v.16 no.2
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• pp.11-23
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• 2013

The main purpose of this study was to examine the mediating effect of participation motivation of continuing professional development between the level of organizational learning transfer climate and learning transfer. In the analysis of the relationship among the level of the organizational learning transfer climate, learning transfer and participation motivation of CPD, organizational learning transfer climate had indirect influence on learning transfer through participation motivation of CPD. Based upon the findings of this study, several suggestions were made to improve professional engineers' participation and learning transfer in CPD and implement future research on professional engineer's CPD.

• Journal of IKEEE
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• v.23 no.4
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• pp.1408-1414
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• 2019

Deep artificial neural network with transfer learning is applied to compressed sensing cardiovascular MRI. Transfer learning is a method that utilizes structure, filter kernels, and weights of the network used in prior learning for current learning or application. The transfer learning is useful in accelerating learning speed, and in generalization of the neural network when learning data is limited. From a cardiac MRI experiment, with 8 healthy volunteers, the neural network with transfer learning was able to reduce learning time by a factor of more than five compared to that with standalone learning. Using test data set, reconstructed images with transfer learning showed lower normalized mean square error and better image quality compared to those without transfer learning.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.165-165
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• 2023

Streamflow prediction is a critical task in water resources management and essential for planning and decision-making purposes. However, the streamflow prediction is challenging due to the complexity and non-linear nature of hydrological processes. The transfer learning is a powerful technique that enables a model to transfer knowledge from a source domain to a target domain, improving model performance with limited data in the target domain. In this study, we apply the transfer learning using the Informer model, which is a state-of-the-art deep learning model for streamflow prediction. The model was trained on a large-scale hydrological dataset in the source basin and then fine-tuned using a smaller dataset available in the target basin to predict the streamflow in the target basin. The results demonstrate that transfer learning using the Informer model significantly outperforms the traditional machine learning models and even other deep learning models for streamflow prediction, especially when the target domain has limited data. Moreover, the results indicate the effectiveness of streamflow prediction when knowledge transfer is used to improve the generalizability of hydrologic models in data-sparse regions.

• Journal of Korea Multimedia Society
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• v.21 no.10
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• pp.1195-1202
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• 2018

Artificial neural network is widely used for its excellent performance and implementability. However, traditional neural network needs to learn the system from scratch, with the addition of new input data, the variation of the observation environment, or the change in the form of input/output data. To resolve such a problem, the technique of transfer learning has been proposed. Transfer learning constructs a newly developed target system partially updating existing system and hence provides much more efficient learning process. Until now, transfer learning is mainly studied in the field of image processing and is not yet widely employed in acoustic data processing. In this paper, focusing on the scalability of transfer learning, we apply the concept of transfer learning to the problem of guitar chord classification and evaluate its performance. For this purpose, we build a target system of convolutional neutral network (CNN) based 48 guitar chords classification system by applying the concept of transfer learning to a source system of CNN based 24 guitar chords classification system. We show that the system with transfer learning has performance similar to that of conventional system, but it requires only half the learning time.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.22 no.3
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• pp.297-307
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• 2015

Purpose: The purpose of this study was to identify the learning style, learning efficacy, transfer of learning, and confidence in performance competence of Core Basic Nursing Skills (CBNS) and factors influencing confidence in performance competence of CBNS by nursing students. Methods: A descriptive study design was used. Participants were 148 nursing students. Data were analyzed using SPSS 20.0 for descriptive statistics, ANCOVA, correlation and regression. Results: Learning styles of the participants were assimilator 33.11%, accommodator 26.35%, diverger 23.65%, and converger 16.89%. Learning efficacy was significantly different according to learning styles, however, transfer of learning and confidence in performance competence of CBNS were not significantly different according to learning styles. Confidence in performance competence of CBNS positively correlated with transfer of learning and learning efficacy. Transfer of learning was a significant predictor of confidence in performance competence of CBNS. Conclusion: The findings of this study indicate that transfer of learning influences confidence in performance competence of CBNS. Thus, nursing faculty should develop educational strategies to enhance and improve transfer of learning, and development of effective confidence in performance competence of CBNS programs.

• The Journal of Information Systems
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• v.15 no.2
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• pp.1-29
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• 2006

The recent development of IT, consolidation of communication and multimedia technology have brought enormous changes in many organizations. Theses changes are enabling the new educational opportunities such as distance teaming and virtual class room. Recently, e-Learning has grown rapidly in business training field, In the context of companies, e-Learning has merits in terms of access convenience, costs reduction self-directed learning, reciprocity, and flexibility. In this regard, the primary purpose of this study is to investigate which factors of e-Learning influence the effectiveness of education and transfer of loaming in business organizations. Based on the prior studies of the education and business training field, research model and research hypotheses were developed. Factors studied in this paper were as follows: 1) learners' characteristics, 2) organizational support and 3) system environments. The results of our study are as follows. (1) Motivation perceived usefulness in Learner factors had an significant influence on both learning effectiveness and transfer of teaming, whereas Ability, expectation had an influence on transfer of teaming. (2) Support from peer, support from supervisor in Organization factors had an significant influence on both Loaming effectiveness and transfer of teaming, whereas support from organization had influence on learning effectiveness. (3) Appropriate contents in system circumstance had an significant influence on both teaming effectiveness and transfer of teaming, whereas interface design had an influence on learning effectiveness.

• Asian Journal of Innovation and Policy
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• v.7 no.2
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• pp.310-337
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• 2018

Adoption of mobile learning (m-learning) is not new in Malaysian oil and gas industry, with heavy investment into research and development to train the workers. Nevertheless, the low application of learnt skills on the job remains an emergent research area where there is a missing link on the effects of m-learning and effective organisational learning and implication on its training transfer. The result of this quantitative research revealed that all variables in m-learning were found to have a positive relationship with the effective organisational learning, and there is evidence of training transfer as a mediator of the relationship between self-directed learning, training design, work environment and effective organisational learning. However, there were some discrepancies in the extend of training transfer between trainee characteristics and organisational learning. As such, some important issues emerged which challenge the importance of evaluating workers' readiness and transfer for a successful implementation of m-learning towards developing effective organisational learning.

• Korean Journal of Occupational Health Nursing
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• v.29 no.4
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• pp.354-362
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• 2020

Purpose: This study aimed to define and clarify learning transfer in nursing. Methods: This study used a hybrid model to analyze the concept of learning transfer in nursing through three phases. For the theoretical phase, learning transfer attributes were identified through a scoping literature review. In the fieldwork phase, in-depth focus group interviews were conducted to develop attributes. Purposive sampling was performed with ten participants(five nursing students, two nurses, three nursing faculty members). In the analysis phase, the attributes and final analysis of learning transfer in nursing were extracted and integrated from the previous two phases. Results: According to the analysis, learning transfer was represented in two dimensions with eight attributes. The development of competency dimension had three attributes: 1) theory acquisition, nursing skills, professional attitude, 2) integration, and 3) analysis competency. The competency change dimension had five attributes: 1) appropriateness in patient care, 2) proficiency in patient care, 3) satisfaction, 4) achievement, and 5) confidence. Conclusion: The concept analysis might provide a basic understanding of learning transfer, a development framework toward a measurement of nursing learning transfer and effective educational nursing strategies.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.909-914
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• 2024

This study suggests a quantitative evaluation of transfer learning, which is widely used in various AI fields, including image recognition for robot vision. Quantitative and qualitative analyses of results applying transfer learning are presented, but transfer learning itself is not discussed. Therefore, this study proposes a quantitative evaluation of transfer learning itself based on MNIST, a handwritten digit database. For the reference network, the change in recognition accuracy according to the depth of the transfer learning frozen layer and the ratio of transfer learning data and pre-training data is tracked. It is observed that when freezing up to the first layer and the ratio of transfer learning data is more than 3%, the recognition accuracy of more than 90% can be stably maintained. The transfer learning quantitative evaluation method of this study can be used to implement transfer learning optimized according to the network structure and type of data in the future, and will expand the scope of the use of robot vision and image analysis AI in various environments.

• Knowledge Management Research
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• v.9 no.4
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• pp.1-16
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• 2008

The purpose of the present study is to explain the effects of academic self-efficacy, self-regulated learning and online task value on academic achievement and learning transfer in corporate cyber education. 202 students who completed S corporate's cyber courses in 2007 and responded to all survey participated in this study. A hypothetical model was proposed, which was composed of academic self-efficacy, online task value and self-regulated learning factors as prediction variables, and learning transfer as well as academic achievement factors as outcome variables. The results of this study through regression analysis as follows. First, learners' academic self-efficacy, self-regulated learning and online task value predict learners' academic achievement significantly. Second, except for academic self-efficacy, learners' self-regulated learning and online task value predict on learners' learning transfer significantly. Third, academic achievement plays a role as mediating value in predicting academic achievement by online task. It implies that learners' academic self-efficacy, online task value and self-regulated learning which predict learners' academic achievement and learning transfer should be considered in developing strategies for the design and operation of cyber courses.