• The Journal of Korean Society for School & Community Health Education
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• v.23 no.2
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• pp.39-50
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• 2022

Objectives: This study aimed to confirm the association between self-determination and self-directed learning among Korean nursing students, as well as the mediating effect of academic self-efficacy. Methods: Data from 139 nurse students were surveyed in this descriptive cross-sectional study. They were collected from Oct 1 to 30, 2020, using self-report questionnaires. The collected data were analyzed using descriptive statistics, independent t-test, one-way analysis of variance, Scheffé test, Pearson's correlation coefficient analysis, and mediated model for PROCESS macro using the SPSS/WIN 24.0. Results: Self-directed learning was positively associated with self-determination (r=.56, p<.001) and academic self-efficacy (r=.63, p<.001). Furthermore, academic self-efficacy had a mediating effect on the relationship between self-determination and self-directed learning (B=0.21, 95% CI=0.12~0.32). Conclusion: The impact of self-determination on the self-directed learning among nursing students was mediated by academic self-efficacy. Therefore, these results provide important data for future self-directed learning in nursing education.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.311-318
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• 2020

This study was done to explore the correlation among self-determination, learning commitment and learning outcomes and identify factors related learning outcomes under academic credit bank system. The survey was conducted self-report questionnaire. The data collection period was form April to November 2018. Participants were 144 registered nurses working currently under academic credit bank system. Collected data were analyzed using SPSS/WIN 24.0. Learning outcomes had a positive correlation with self-determination and learning commitment. learning commitment. Self-determination was positively correlated learning commitment. Factors affecting learning outcomes included self-determination and learning commitment. And self-determination and learning commitment accounted for 32.1% of the variance in learning outcomes. Therefore, developing learning outcomes support for improving self-determination and learning commitment. Future research will be needed to clarify the effects of learning outcomes promotion program on self-determination and learning commitment.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.11-19
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• 2015

The purpose of this research was to investigate factors influencing learning outcomes and effects of self-directed learning ability and motivation on learning satisfaction of nursing students in blended learning environment. A survey was used, and the subjects were 140 undergraduate students participated in Adult Nursing lesson at D University. Data collected from 9 to 14 June, 2014. There were no significant differences among general characteristics. Self-directed learning ability and learning satisfaction had significant correlations with each other (r=.25, p=.003). Self-directed learning ability was significantly associated with learning satisfaction, explaining 22.1% of the variance (F=20.74, p<.001). The results suggest that further research is needed to consider self-directed learning ability of students and to test the advantages of blended learning in developing contents in blended learning.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.973-980
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• 2023

This study analyzed the effects of a Micro-learning content-based extracurricular program among university students based on their general characteristics. A survey was conducted on 600 students affiliated with G University, a major national university. Learning immersion and learning motivation were used as the key indicators for measuring the learning effects. Cronbach's α coefficient analysis was performed to validate the reliability of the learning effect measurement tool. Independent sample t-tests were utilized to analyze differences in learning immersion and learning motivation based on gender and major disciplines. One-way analysis of variance (ANOVA) was employed to measure differences in learning immersion and learning motivation according to academic year. According to the research findings, gender and academic year did not significantly influence participation in the Micro-learning content-based program. However, differences in learning immersion and learning motivation were observed depending on the major discipline. Based on this, it is suggested that future programs should provide suitable environments and stimuli based on the students' major disciplines.

• Safety and Health at Work
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• v.6 no.3
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• pp.211-217
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• 2015

Background: E-learning-based programs have recently been introduced to the occupational safety and health (OSH) education for migrant workers in Korea. The purpose of this study was to investigate how the factors related to migrant workers' backgrounds and the instructional design affect the migrant workers' satisfaction with e-learning-based OSH education. Methods: The data were collected from the surveys of 300 migrant workers who had participated in an OSH education program. Independent sample t test and one-way analysis of variance were conducted to examine differences in the degree of learning satisfaction using background variables. In addition, correlation analysis and multiple regression analysis were conducted to examine relationships between the instructional design variables and the degree of learning satisfaction. Results: There was no significant difference in the degree of learning satisfaction by gender, age, level of education, number of employees, or type of occupation, except for nationality. Among the instructional design variables, "learning content" (${\beta}=0.344$, p < 0.001) affected the degree of learning satisfaction most significantly, followed by "motivation to learn" (${\beta}=0.293$, p < 0.001), "interactions with learners and instructors" (${\beta}=0.149$, p < 0.01), and "previous experience related to e-learning" (${\beta}=0.095$, p < 0.05). "Learning environment" had no significant influence on the degree of learning satisfaction. Conclusion: E-learning-based OSH education for migrant workers may be an effective way to increase their safety knowledge and behavior if the accuracy, credibility, and novelty of learning content; strategies to promote learners' motivation to learn; and interactions with learners and instructors are systematically applied during the development and implementation of e-learning programs.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.55-61
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• 2010

In this paper, we proposed selective extraction method of frequency information and PCA-ELM based diagnosis system for three-phase induction motors. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by variance As the next step, feature extraction is performed by principal component analysis (PCA). Finally, we used the classifier based on Extreme Learning Machine (ELM) with fast learning procedure. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.143-149
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• 2022

This excerpt delineates a quantitative measure of relationship between a research title and its respective abstract extracted from different journal articles documented through a Korean Citation Index (KCI) database published through various journals. In this paper, we propose a machine learning-based similarity metric that does not assume normality on dataset, realizes the imbalanced dataset problem, and zero-variance problem that affects most of the rule-based algorithms. The advantage of using this algorithm is that, it eliminates the limitations experienced by Pearson correlation coefficient (r) and additionally, it solves imbalanced dataset problem. A total of 107 journal articles collected from the database were used to develop a corpus with authors, year of publication, title, and an abstract per each. Based on the experimental results, the proposed algorithm achieved high correlation coefficient values compared to others which are cosine similarity, euclidean, and pearson correlation coefficients by scoring a maximum correlation of 1, whereas others had obtained non-a-number value to some experiments. With these results, we found that an effective title must have high correlation coefficient with the respective abstract.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2527-2545
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• 2016

Measuring the similarity of given samples is a key problem of recognition, clustering, retrieval and related applications. A number of works, e.g. kernel method and metric learning, have been contributed to this problem. The challenge of similarity learning is to find a similarity robust to intra-class variance and simultaneously selective to inter-class characteristic. We observed that, the similarity measure can be improved if the data distribution and hidden semantic information are exploited in a more sophisticated way. In this paper, we propose a similarity learning approach for retrieval and recognition. The approach, termed as LDA-FEK, derives free energy kernel (FEK) from Latent Dirichlet Allocation (LDA). First, it trains LDA and constructs kernel using the parameters and variables of the trained model. Then, the unknown kernel parameters are learned by a discriminative learning approach. The main contributions of the proposed method are twofold: (1) the method is computationally efficient and scalable since the parameters in kernel are determined in a staged way; (2) the method exploits data distribution and semantic level hidden information by means of LDA. To evaluate the performance of LDA-FEK, we apply it for image retrieval over two data sets and for text categorization on four popular data sets. The results show the competitive performance of our method.

• Korean Medical Education Review
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• v.13 no.1
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• pp.45-49
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• 2011

This study was done to analyze students' learning and its lasting effect by teaching strategy involving questioning. This study was performed with 68 students who were enrolled in a physiology class of the Yonsei University College of Medicine in Seoul, Korea, in 2003. The students were randomly divided into 2 groups. One group was taught in a way where students asked questions and the instructor answered the questions. For the other group of students, the instructor asked questions, and the students answered the questions. We performed a pre-test before the study begins and post-tests immediately, 3 weeks, and 6 weeks after the study. The results were analyzed by using analysis of covariance and repeated measures analysis of variance. A higher learning effect was observed in a group where questions were asked by students compared with the other group. The post-test results showed no significant difference in the lasting effect of learning according to the teaching strategy. Students' learning significantly improved when students asked questions and the instructor answered the questions compared with the strategy of the instructor asking questions and students answering to the questions.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.24-39
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• 2022

Online learning is becoming ubiquitous worldwide because of its accessibility anytime and from anywhere. However, it cannot be successfully implemented without understanding constructs that may affect its adoption. Unlike previous literature, this research extends the Unified Theory of Acceptance and Use of Technology with three well-known theories, namely compatibility, online self-efficacy, and knowledge sharing and acquisition to examine online learning adoption. A total of 264 higher education students took part in this research. Partial Least Squares-Structural Equation Modeling was used to evaluate the proposed theoretical model. The findings suggested that performance expectancy and compatibility were significant predictors of behavioral intention, whereas behavioral intention, facilitating conditions, and compatibility had a significant and direct effect on online learning's actual use. The results also showed that knowledge acquisition, knowledge sharing, and online self-efficacy were determinates of performance expectancy. Finally, online self-efficacy was a predictor of effort expectancy. The proposed model achieved a high fit and explained 47.7%, 75.1%, 76.1%, and 71.8% of the variance of effort expectancy, performance expectancy, behavioral intention, and online learning actual use, respectively. This study has many theoretical and practical implications that have been discussed for further research.