• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.399-405
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• 2020

This study examined the effect service learning combined with self-directed study had on transferring skills from the university classroom to their practical application in local community centers. The subjects of this study were students who took service learning classes from September 1, 2019 to December 28, 2019. The research question in this paper is 'What is the relationship between service learning-based general classes and self-directed learning?'. That is, how do service learning-based general classes affect sub-elements of self-directed learning? We then tried to determine how the variables of individual learners can affect self-directed learning ability. The results showed that autonomy and problem solving were the greatest at r=.66. Openness and self-assessment (r=.60), autonomy and self-assessment (r=.55) had significant correlation. Learner autonomy had a significant correlation with facilitation and collaboration of service learning (**p<.01). According to this result of the study, it is possible for learners to deepen what they have learned at school and to practice and gain experience through community service. Further, practical problem solving and self-assessment through reflection are possible. Learners were able to inspire responsibility as members of society and increase self-esteem as democratic citizens.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.421-422
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• 2015

Deep learning, a deepen neural network technology that demonstrates the enhanced performance of neural network analysis, has been getting the spotlight in recent years. The present study proposed a process to test the error rates of certain variables and predict big data by using R, a analysis visualization tool based on deep learning, applying the RBM(Restricted Boltzmann Machine) algorithm to deep learning. The weighted value of each dependent variable was also applied after the classification of dependent variables. The investigator tested input data with the RBM algorithm and designed a process to detect error rates with the application of R.

• Journal of Information Technology Services
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• v.10 no.1
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• pp.151-165
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• 2011

Recently building up of u-learning oriented teaching and learning system has been expanded rapidly, However domestic junior college's challenging for adapting it might be slower than other educational body's doing, and in that result it might be paid more or be taken longer time to improve their old system effectively. Now, it is very time for them to develop and implement u-learning oriented teaching and learning system quickly. This paper offers and draws the core factors to design ubiquitous teaching and learning model systematically through investigation of worldwide recent technology and R&D, patent, service and standardization tendency related with u-learnig modeling.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.335-342
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• 2023

This study was attempted to confirm the effects of learning flow, academic stress, and resilience on self-efficacy in college students. This study targeted 304 university students in B and G cities. Data analysis was analyzed by descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis using the SPSS 22.0 program. The average self-efficacy of the subjects was 3.14±0.62 points, and the difference in self-efficacy according to general characteristics was significant in gender(t=-2.533, p=.012) and satisfaction with major(F=5.687, p=.004). Self-efficacy of the subjects was positive correlation with learning flow(r=.574, p<.001), resilience(r=.525, p<.001), and negative correlation with academic stress(r=-.262, p<.001). Resilience of the subjects showed positive correlation with learning flow(r=.325, p<.001) and negative correlation with academic stress(r=-.291, p<.001). Learning flow showed negative correlation with academic stress(r=-.211, p<.001). Factors influencing the self-efficacy of the subjects were identified as academic commitment (β=.442, p<.001) and resilience (β=.363, p<.001) and the explanatory power was 45.6%. Therefore, in order to improve college students' self-efficacy, it is necessary to develop and apply education and programs that can improve learning flow and resilience.

• Journal of The Korean Society of Integrative Medicine
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• v.9 no.1
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• pp.141-150
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• 2021

Purpose : The purpose of this study was to investigate the effect of case-based learning application in human growth development classes on nursing students' problem-solving ability, self-directed learning ability, and academic self-efficacy. Methods : The research method was a self-report questionnaire before and after case-based learning for second-year nursing students who took the human growth development course at U University in K city. The collected data were statistically processed using SPSS WIN 21.0. Results : The results of the study showed that after case-based learning, problem-solving ability, self-directed learning ability, and academic self-efficacy were all significantly improved. In addition, as a result of examining the correlation between each variable after case-based learning, problem solving ability score and self-directed learning ability score (r=.54, ｐ<.01), and problem solving ability scores and academic self-efficacy scores (r=.44, ｐ<.01), were significantly correlated with self-directed learning ability scores and the academic self-efficacy reduction scores (r=.76, ｐ<.01). Conclusion : The results of this study suggested the need for various learning programs such as case-based learning to improve nursing students' problem-solving abilities and self-directed learning abilities and their application. In addition, to improve the learning self-efficacy of nursing students, a continuous and systematic study is suggested to develop and apply customized educational programs according to the learners' preferences. Since the sample group in this study was limited to one university, there were few cases and no control group, so there are limitations in generalizing the test effect, However, significant differences a were verified in the case-based learning pre-tests and post-tests.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.407-416
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• 2019

This study was to investigate the factors affecting the learning strategies of the students in the Department of Health Science. The data collection was conducted through a self-reporting questionnaire to 373 college students majoring in health and health care at two universities in J.do. and the collected data was analyzed using the SPSS/WIN 23.0 program. The study found that there were significant differences in learning strategies depending on Motive of application and major satisfaction level, and there were significant differences in academic stress with the grade, gender, Motive of application, major satisfaction, and academic self-efficiency, and self-directed learning depending on the grade, motive of application, and major satisfaction level. The results showed that the correlation between learning strategy and academic self-efficacy(r=.478, p<.001) and self-directed learning(r=645, p<.001), academic stress(r=-.193, p<.001). Self-directed learning(${\beta}=0.61$), major satisfaction (satisfaction) (${\beta}=0.31$), and major satisfaction (usually)(${\beta}=0.24$) affect the learning strategy, with 42.6% overall explanation. Based on the results of the study, the search for ways to improve major satisfaction levels and self-directed learning skills could have a positive impact on improving the learning strategy of college students in the health sector.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.267-278
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• 2024

This study was a descriptive research study targeting medical students to determine the impact of self-directed learning ability, self-regulated learning, and communication ability on self-efficacy in performing medical treatment. This study randomly selected medical students from Region J, located in Province G, as the approximate population, and a total of 125 copies were finally analyzed. Descriptive statistics were analyzed using t-test, ANOVA, correlation, and multiple regression analysis using IBM SPSS/25. Self-efficacy in performing medical treatment was related to self-directed learning ability (r=.61, p<.001), self-regulated learning (r=.50, p<.001), and communication ability (r=.33, p<.001). There was a positive correlation with all of them. As a result of analyzing the variables that affect the subject's self-efficacy in performing medical treatment using hierarchical multiple regression, self-directed learning ability was found to be the factor that best predicts self-efficacy in performing medical treatment, followed by self-regulated learning and communication ability. The total explanatory power was 46.6%. Acquiring specialized knowledge and becoming a doctor after graduation through clinical practice and acquiring the basic clinical practice skills necessary to successfully perform one's duties are important tasks that medical students must accomplish. Therefore, in order to improve medical students' self-efficacy in performing medical treatment, the importance of improving health care, major satisfaction, and life satisfaction must be recognized and managed. In addition, efforts to develop programs and improve systematic systems that can improve self-directed learning, self-regulated learning, and communication skills should also be supported.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.527-535
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• 2003

A behavior-based control and learning architecture is proposed, where reinforcement learning is applied to learn proper associations between stimulus and response by using two types of memory called as short Term Memory and Long Term Memory. In particular, to solve delayed-reward problem, a knowledge-propagation (KP) method is proposed, where well-designed or well-trained S-R(stimulus-response) associations for low-level sensors are utilized to learn new S-R associations for high-level sensors, in case that those S-R associations require the same objective such as obstacle avoidance. To show the validity of our proposed KP method, comparative experiments are performed for the cases that (ⅰ) only a delayed reward is used, (ⅱ) some of S-R pairs are preprogrammed, (ⅲ) immediate reward is possible, and (ⅳ) the proposed KP method is applied.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.2
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• pp.144-151
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• 2021

Purpose: The purpose of this study was to investigate the mediating effect of optimism on the relationship between grit and learning flow in nursing college students. Methods: Structured self-reported questionnaires were used to measure grit, optimism and learning flow. The study was conducted on 200 nursing students in P, U and J cities between September 1 and September 20, 2020. The data were analyzed using a t-test, one-way ANOVA, Pearson's correlation coefficients and hierarchical multiple linear regression with SPSS/WIN 23.0. Results: Significant relationships were found between learning flow and grit (r=.60, p<.001), between learning flow and optimism (r=.42, p<.001), and between grit and optimism (r=.42, p<.001). Additionally, optimism had a partial mediating effect on the relationship between grit and learning flow (Z=3.11, p<.001). Conclusion: These results indicate that interventions to increase the level of grit along with optimism is necessary in order to increase the level of nursing college students' learning flow.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.