• Education of Primary School Mathematics
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• v.18 no.1
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• pp.31-44
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• 2015

This study aims to test effect of transfer learning program rather than students' transfer ability. For these purpose, firstly this study design transfer learning program to apply from 'rate concept' in learning math class to 'velocity concept' in science class. Subsequently, this study is to analyze whether this program affect on 'the rate concept understanding' and 'the mathematics learning attitude'. Followings are the findings from this study. First, transfer learning program affect on improving students' rate concept understanding. Moreover, 17 among 35 students' who stay in 'ratio level' move to 'internalized ratio level'. Second, besides transfer learning program is not only cause to change students' learning attitude, this program impact on changing their learning attitude positively. The study has an important implications in that it designed new learning program that students experience transfer and test its effect.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.262-270
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• 2021

The purpose of this study was to identify factors influencing the transfer of learning for nursing students in clinical practice. This study is a descriptive survey research conducted with 113 nursing students. Self-directed learning, motivation to transfer, and transfer of learning were measured. Data were analyzed by descriptive analysis, independent t-test, and ANOVA. The transfer of learning were significantly different according to the interpersonal relationship (t=10.43, p=.002), the satisfaction of nursing major (t=3.81, p=.006), satisfaction of nursing skills laboratory (t=4.61, p=.004). Transfer of learning had a correlation with self-directed learning, motivation (r=.46, p=<.001), and motivation to transfer (r=.60, p=<.001). In addition, motivation to transfer, the satisfaction of nursing skills laboratory, and learning evaluation were significant predictors of transfer of learning. Finally, in order to increase the transfer of learning for nursing students, nursing instructors need to encourage motivation to transfer, and to apply educational strategies that increase self-directed learning, as well as the satisfaction of the nursing skills laboratory.

• Journal of Engineering Education Research
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• v.27 no.3
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• pp.26-33
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• 2024

This study aimed to verify the learning effectiveness of students who took courses in the field of new and renewable energy, which have been operated within a convergence university system. To achieve this, data were collected from 1,228 students who participated in 34 courses jointly developed and conducted by seven universities as part of standard curriculum offerings. The study analyzed learning effectiveness (course satisfaction, transfer motivation, learning transfer, creativity-convergence competency) using Excel 2018 and SPSS 25.0. It also examined inter-university differences in learning effectiveness and identified factors influencing creativity-convergence competency. The main findings are as follows: (a) Course satisfaction (M= 4.20), transfer motivation (M=3.62), learning transfer (M= 4.06), and creativity-convergence competency (M=3.92) were generally high. (b) Analysis of learning effectiveness differences between universities showed no significant differences among universities A, B, C, D, and E. University F was lower compared to other universities, while University G was significantly higher than others. (c) Sex, grade, number of courses taken, course satisfaction, transfer motivation, and learning transfer had effect on creativity-convergence competency. The results of this study provided implications for promoting activities to attract students, expanding transfer opportunities, and ensuring student agency.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.245-251
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• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• Korean Security Journal
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• no.51
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• pp.61-78
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• 2017

This study aims to empirically validate the relationship between organizational learning transfer climate and the transfer of training and to enhance the transfer of training among South Korean coast guards. The empirical data was collected through 526 South Korean coast guards admitted to the institute, and support by managers and peers, and potential for organizational change were selected as independent variables for multiple regression. As a result, the transfer of training is positively correlated with support of mangers and peers, and potential for organizational change, thus suggesting factors like supervisor participation and long-term educational planning as policy implications for the effective transfer of training to work environment. Though findings from research cannot be generalized to the broader population due to limitations of sampling, this study does find its significance in that organizational learning transfer climate was considered as a key factor influencing the transfer of learning for the first time.

• Journal of Educational Research in Mathematics
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• v.12 no.1
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• pp.29-48
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• 2002

The purpose of this paper is to investigate the transfer in mathematics learning, especially focussing on arithmetic and algebra. There are many obstacles at the stage of transfer in learning. In the case of mathematics, each learning contents are definitely categorized by the learning level, therefore these obstacles are more happened than other subjects. First of all, this paper investigates the historical transfer from arithmetic to algebra by Sfard's perspectives. And we define prealgebra as the stage between arithmetic and algebra, which may be revised obstacles or misconceptions happened in the early algebra learning. Also, this paper discusses various obstacles and concrete examples happened in the transfer from arithmetic to algebra. To advance the understanding in the learning of algebra, we consider the core contents of the algebra learning which should be stressed at the prealgebra stage. Finally we present the teaching units of (pre)algebra which are sequenced from the variable concepts to equations.

• East Asian Journal of Business Economics (EAJBE)
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• v.5 no.4
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• pp.20-38
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• 2017

Purpose - this study analyzes the impact of restaurant startup education on transfer effects in learning and entrepreneurial intentions based on previous research. Also, problems and ways to provide effective business startup education for a restaurant founder will be proposed based on the result. Research design, data, methodolog - this study collected surveys by conducting direct investigation. From July 20th of 2016 to September 20th of 2016 (approximately 60 days), the survey was collected. Out of 540 surveys, 520 were collected. And excepting 9 surveys which were untrustworthily conducted, total 511 surveys were used for the analysis. Results - First, as a result of the impact of which factor of a restaurant founder's startup education has a positive impact on transfer effect in learning (the satisfaction of startup education and learning transfer), law education, entrepreneurship education and business district analysis education and practical education have turned out be positively related variables. Secondly, as a result of the impact of a restaurant founder's startup education satisfaction on transfer in learning, it has been identified that startup education has a positive impact. Lastly, by conducting an analysis to find out which factor from a restaurant founder's transfer effect in learning has an impact on entrepreneurial intention, all variables, including startup education satisfaction and transfer effect in learning, are positively influencing factors. Conclusions - as startup education satisfaction of a restaurant founder is increasing, there is a higher level of transfer effect in learning. Moreover, as transfer effect of startup business is getting higher, it has an impact on entrepreneurial intention.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.482-489
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• 2013

The purpose of this study was to identify the relationship of instructional design, perceived learning transfer, and satisfaction. The data were collected using questionnaire from the sample of 239 nursing students. The level of learning transfer was explained by introduction with learning context & providing guidance and initial attention. The level of learning transfer was explained by learning object with motivation, learning goal alignment, accessibility and feedback & adaptation. The level of program satisfaction was explained by introduction with learning context & providing guidance and initial attention. The level of program satisfaction was explained by learning object with motivation, presentation design, interaction availability, feedback & adaptation, learning goal alignment and contents quality. The findings serve as basic data to design e-Learning program to improve learning transfer and satisfaction.

• Smart Media Journal
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• v.11 no.4
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• pp.30-37
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• 2022

Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this issue and observed comparable results. However, current transfer learning strategies can be regarded as a supervised method as it hypothesizes that there are many labeled images in a source domain. In contrast, unsupervised transfer learning, using only images in a source domain, gives more convenience as collecting images is much easier than annotating. In this paper, we leverage unsupervised transfer learning to perform plant disease recognition, by which we achieve a better performance than supervised transfer learning in many cases. Besides, a vision transformer with a bigger model capacity than convolution is utilized to have a better-pretrained feature space. With the vision transformer-based unsupervised transfer learning, we achieve better results than current works in two datasets. Especially, we obtain 97.3% accuracy with only 30 training images for each class in the Plant Village dataset. We hope that our work can encourage the community to pay attention to vision transformer-based unsupervised transfer learning in the agricultural field when with few labeled images.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.4
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• pp.474-484
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• 2024

Reinforcement learning, which are also studied in the field of defense, face the problem of sample efficiency, which requires a large amount of data to train. Transfer learning has been introduced to address this problem, but its effectiveness is sometimes marginal because the model does not effectively leverage prior knowledge. In this study, we propose a stochastic initial state randomization(SISR) method to enable robust knowledge transfer that promote generalized and sufficient knowledge transfer. We developed a simulation environment involving a cooperative robot transportation task. Experimental results show that successful tasks are achieved when SISR is applied, while tasks fail when SISR is not applied. We also analyzed how the amount of state information collected by the agents changes with the application of SISR.