• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.581-592
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• 2020

This research aims to explore the effect of participation in 'peer tutoring(learning tutoring)' program designed for low achiever students, and to provide an explanation for the improvement of related extracurricular activity. For this, firstly, the study analyzed differences between goal attainment group and non-attainment group in self-directed learning readiness, learning presence and learning transfer. Secondly, the relationships between three variables were analyzed. Based on an online survey of 154 low achievers participating in learning tutoring, two research questions were examined using t-test, correlation and hierarchical multiple regression analyses. Our findings show that firstly, the academic achievement after participating in tutoring improved more than before. Secondly, there were differences in three variables by gender and grades. Also, there were differences in three variables between two groups. Finally, there was a high positive correlation between three variables, and 71% of learning transfer was explained by self-directed learning readiness and learning presence. Based on these findings, the practical implications are discussed regarding the improvement of tutoring program.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.111-134
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• 2017

This study aims to identify factors to the effectiveness of librarians' continuing education, which is regarded as learning transfer, and to examine the relationship between the effectiveness and the factors identified. Learning transfer is defined as an intention to apply the lessons learned in the education to the learner's practice. For this purpose, a questionnaire survey was conducted to investigate continuing education experiences of university librarians. Findings indicate that characteristics of individual librarians, educational program, and organizational environment have to be taken into consideration to manage librarians' continuing education effectively. Therefore, it is suggested that the university library as an organization should control pre- and post-activities of continuing librarians' education at the library in order to maximize the learning transfer as the effectiveness of the education. Suggestions are developed with factors to the learning transfer to improve the effectiveness.

• Management & Information Systems Review
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• v.32 no.3
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• pp.43-59
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• 2013

The most important thing in training of organization is that how effectively it can be made most of the performance among the staff. It will be useless if the knowledge which gaining after training can not be applied. Therefore the transfer of learning is studied since it is important for decision of training. We studied the factors of transfer of learning and carried out a survey targeting the public officials of Gangwon province with the factors we made a study. We define the factor of both promoted and interrupted in training and suggest the way of improving it. The first, the modeling of competency can stimulate the desire of achievement and complete a course of training among staff of organizations. The second, the construction of training program and organizational culture just for Gangwon province can increase the satisfaction of training among the learners. The third, the establishment of management system after training can reinforce the capability making use of train. The sharing of each information with boss at the office can help to stimulate the function of feedback after training as well.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.339-348
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• 2021

In recent software defect prediction research, defect prediction between cross projects and cross-version projects are actively studied. Cross-version defect prediction studies assume WP(Within-Project) so far. However, in the CV(Cross-Version) environment, the previous work does not consider the distribution difference between project versions is important. In this study, we propose an automated Bayesian optimization framework that considers distribution differences between different versions. Through this, it automatically selects whether to perform transfer learning according to the difference in distribution. This framework is a technique that optimizes the distribution difference between versions, transfer learning, and hyper-parameters of the classifier. We confirmed that the method of automatically selecting whether to perform transfer learning based on the distribution difference is effective through experiments. Moreover, we can see that using our optimization framework is effective in improving performance and, as a result, can reduce software inspection effort. This is expected to support practical quality assurance activities for new version projects in a cross-version project environment.

• Journal of Information Technology Applications and Management
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• v.28 no.4
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• pp.33-43
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• 2021

While youth unemployment has recorded the lowest level since the global COVID-19 pandemic, SMEs(small and medium sized enterprises) are still struggling to fill vacancies. It is difficult for SMEs to find good candidates as well as for job seekers to find appropriate job offers due to information mismatch. To overcome information mismatch, this study proposes the fine-turning model for bidirectional HR matching based on a pre-learning language model called BERT(Bidirectional Encoder Representations from Transformers). The proposed model is capable to recommend job openings suitable for the applicant, or applicants appropriate for the job through sufficient pre-learning of terms including technical jargons. The results of the experiment demonstrate the superior performance of our model in terms of precision, recall, and f1-score compared to the existing content-based metric learning model. This study provides insights for developing practical models for job recommendations and offers suggestions for future research.

• The Journal of Korean Institute for Practical Engineering Education
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• v.1 no.1
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• pp.7-12
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• 2009

The worldwide spread of the internet has made it an important topic in our everyday lives. It has not only changed ordinary lives, but also the whole spectrum of modern society. Thus, it is necessary to understand the characteristic changes in learners as well as social demand in this drastic transformation period and to modify the goal and methods of learning to nurture future intellects. To achieve this, there have been recent attempts to invent new learning methods involving Web 2.0, which focuses on the user. Out of the various aspects of Web 2.0, the blog is expected to invoke learner-oriented education and active discussions between learners, in that the individual manages the blog autonomously and there is much interaction between users. This study will construct a learning system using the blog, apply it on real learners, and analyze its effect on the learners' scholastic achievements, interests, and learning transfer.

• Smart Media Journal
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• v.10 no.1
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• pp.55-62
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• 2021

We applied machine learning of semi-supervised learning, transfer learning, and federated learning as examples of AI use cases that can be applied to the three major industries(Automobile industry, Energy industry, and AI/Healthcare industry) of Gwangju Metro-city, and established an ML strategy for AI services for the major industries. Based on the ML strategy of AI service, practical approaches are suggested, the semi-supervised learning approach is used for automobile image recognition technology, and the transfer learning approach is used for diabetic retinopathy detection in the healthcare field. Finally, the case of the federated learning approach is to be used to predict electricity demand. These approaches were tested based on hardware such as single board computer Raspberry Pi, Jaetson Nano, and Intel i-7, and the validity of practical approaches was verified.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.39-48
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• 2023

Because a railway bridge is designed over a long period of time and covers a large site, it involves various environmental factors and uncertainties. For this reason, design changes often occur, even if the design was thoroughly reviewed in the initial design stage. In particular, design changes of large-scale facilities, such as railway bridges, consume significant time and cost, and it is extremely inefficient to repeat all the procedures each time. In this study, a technique that can improve the efficiency of learning after design change was developed by utilizing the learning result before design change through transfer learning among deep-learning algorithms. For analysis, scenarios were created, and a database was built using a previously developed railway bridge deep-learning-based prediction system. The proposed method results in similar accuracy when learning only 1000 data points in the new domain compared with the 8000 data points used for learning in the old domain before the design change. Moreover, it was confirmed that it has a faster convergence speed.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.77-92
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• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.