• The Journal of Korean Association of Computer Education
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• v.10 no.6
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• pp.79-89
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• 2007

The advent of e-Learning paradigm requires a various type of e-Learning models and systems which are appropriate to support effective teaching-learning process. Accordingly, the teaching-learning system using the Internet and the intelligent tutoring system(ITS) in e-Learning environment has attracted a fair amount of critical attention. However there is a wide gap between infrastructure of a present educational site and the u-learning environment. Therefore, in this paper, an ITS teaching-learning model is proposed and system is developed for a school environment, which is based on a learner's cognitive structure and applies a concept of u-Learning, and then is verified for validity. X-Neuronet, the developed system, offers a method of representing a learner's cognitive structure so as to apply the method for the efficient individualized learning.

• Journal of Digital Convergence
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• v.18 no.1
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• pp.369-377
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• 2020

The purpose of this study is to find out the effect of e-learning quality and learner's usage motivation on e-learning satisfaction in Korea and China. In addition, by comparing and analyzing the factors influencing the satisfaction of learners between the two countries, this study aims to suggest the effective use of e-learning. This study surveyed Korean university students at Y and K universities in Gyeongsangbuk-do and Chinese university students at A university in Henan, China. As a result, for Korean university students, it is showed that learning time, learning space, learning process, usefulness, e-learning information quality, and service quality affect e-learning satisfaction. For Chinese university students, learning time, learning process and e-learning system quality, information quality, and service quality were found to affect e-learning satisfaction. Among them, service quality was an important factor influencing e-learning satisfaction in both countries, but the average score of each factor was very low. In the future, we discussed ways to improve service quality.

• 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 Korean Academy of Nursing
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• v.52 no.3
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• pp.278-290
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• 2022

Purpose: This study aimed to identify the mediating effect of learning flow and the moderated mediation effect of digital literacy on the effect of the learning presence of non-face-to-face class experience in nursing students on academic achievement. Methods: Participants were 272 nursing students from six universities in two different cities. A self-report questionnaire was used to measure learning presence, learning flow, digital literacy, and academic achievement. Analysis was performed using SPSS 26.0 and SPSS PROCESS Macro (4.0). Results: The mediating effect of learning flow on the effect of learning presence on academic achievement was 0.42, and the moderated mediation index of digital literacy was 0.17. Learning flow showed a mediating effect on the relationship between learning presence and academic achievement. Digital literacy had a moderated mediation effect on the relationship between learning presence and academic achievement that was mediated by learning flow. Conclusion: The intensity of the mediating effect of nursing students' learning presence on academic achievement through learning flow increases as the level of digital literacy increases. These results suggest that educational programs considering the level of learning presence, learning flow, and digital literacy are required to promote the academic achievement of nursing college students.

• Journal of Advanced Navigation Technology
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• v.13 no.2
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• pp.296-301
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• 2009

In the self-directed learning, learner can construct learning course. But it is very difficult for learner to construct learning course with understanding the various learning contents's characteristics. This research proposed the method to support to learner the information of learning contents type to fit the learner as calculate the learner's learning preference when learner construct the learning course. The calculating method of learning preference used preference vector value of topic map. To apply this method, we tested 20 learning sampling group and presented that this method help to learner to construct learning course as getting the high average degree of learning satisfaction.

• 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.

• Journal of Korean Home Economics Education Association
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• v.8 no.1
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• pp.51-57
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• 1996

The purpose of this study were to examine the difference of the effect of CAI and students’attitude to Home Economics Class through CAI. 120 girl students of the first year were chosen at B middle school in Daegu. Among them each 30 students were classified into 4 groups； High Intelligence group/Individual learning pattern, High Intelligence group/Small group learning pattern, Low Intelligence group/Individual learning pattern, Low Intelligence group/Small group learning pattern. The task of learning was “management of washing”Unit of the second grade. The data were processed with Cronbach’s ${\alpha}$, t-test, ANOVA by SPSS/PC(sup)+. The research findings are as follows： 1. In the verification of CAI Learning effect according to student group pattern, there is no difference between Individual learning pattern and Small group learning pattern in Achivement and Retention of learning. 2. In the verification of CAI Learning effect according to student intelligence level, there is no difference between High Intelligence group and Low Intelligence group in Achivement and Retention of learning. 3. The result of students’attitude to Home Economics Class verificated is an follows. (1) Individual learning pattern is more attensive than Small group learning pattern, but there is no difference in Intelligence level. (2) Low Intelligence group is more positive than High Intelligence group, and Small group learning pattern is more positive than Individual learning pattern in a view of Home Economics Class after using CAI.

• The Korean Journal of Emergency Medical Services
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• v.25 no.3
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• pp.49-61
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• 2021

Purpose: The study investigated the effects of self-directed learning, learning flow, and academic self-efficacy variables on academic achievement. Methods: This is a descriptive correlation study to understand the effects of self-directed learning, learning flow, and academic self-efficacy on academic achievement. Results: There is a significant positive correlation between the participants' self-directed learning, learning flow, academic achievement, and academic self-efficacy. Self-directed learning and learning flow influenced academic achievement, while academic self-efficacy was found to have a partial mediating effect. As indicated above, academic self-efficacy and self-directed learning were significant predictors of academic achievement. Conclusion: The study results can be used as basic data to conduct future studies. Furthermore, results can inform the development of educational programs that enhance self-directed learning, learning flow, and academic self-efficacy to improve students' academic achievement in the department of emergency technology.

• Journal of Engineering Education Research
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• v.22 no.2
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• pp.3-15
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• 2019

This study analyzed the evaluations of instructors and experts on flipped learning-based engineering in order to suggest improvement strategies. This study was conducted with 8 engineering courses which participated in the flipped learning course development project of C university from 2017-2018. As a result of the analysis, the instructors and experts pointed out that the pre-learning was not performed and checked effectively. In this regard, the instructors suggested the students' burden of pre-learning, the lack of understanding about flipped learning, and the experts suggested the lack of instructional strategies to facilitate pre-learning. In addition, the instructors and the experts pointed out that the courses were still instructor-centered. The instructors evaluated that they operated the instructor-led course by themselves. In addition, the experts suggested that there was not enough instructional strategies to activate the learner-centered activities. The number of the students and the lecture room environment that were not appropriate for the learner- centered class were the evaluation opinions of both the instructors and the experts. In addition, the professor suggested the lack of understanding and preparation of the flipped learning of the instructors and the learner as the main opinion, and the experts pointed out that the online learning system and classroom was not linked for pre-learning, classroom learning, and the post-learning. Based on these results, suggestions for improvement of flip learning based engineering course were suggested.

• Journal of IKEEE
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• v.25 no.4
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• pp.644-649
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• 2021

Self-Imitation Learning is a simple off-policy actor-critic algorithm that makes an agent find an optimal policy by using past good experiences. In case that Self-Imitation Learning is combined with reinforcement learning algorithms that have actor-critic architecture, it shows performance improvement in various game environments. However, its applications are limited to reinforcement learning algorithms that have actor-critic architecture. In this paper, we propose a method of applying Self-Imitation Learning to Deep Q-Network which is a value-based deep reinforcement learning algorithm and train it in various game environments. We also show that Self-Imitation Learning can be applied to Deep Q-Network to improve the performance of Deep Q-Network by comparing the proposed algorithm and ordinary Deep Q-Network training results.