• Educational Technology International
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• v.13 no.2
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• pp.233-255
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• 2012

The purpose of this study is to examine the relationships among the factors that affect technology knowledge, learning strategies with technology, and academic self-efficacy of college students. Technology and its utilizing ability is a critical competency for the learners to acquire to live in the Digital Era of 21st century. However, little is known about how the competency involving technology affects academic self-efficacy. To address the aim of the study, a survey was conducted with 39 questions including technology knowledge, learning strategies with technology, and academic self-efficacy targeting 137 students in A university. The result of the structural equation modeling shows that the technology knowledge of college students indirectly influences the academic self-efficacy. The learning strategies with technology are mediating variable linking technology knowledge with academic self-efficacy. Technology knowledge explains 71% of variance in learning strategies with technology. Therefore, college students need to keep up with knowledge of technology and improve learning strategies with technology to activate academic self-efficacy.

• Journal of Gifted/Talented Education
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• v.19 no.1
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• pp.95-117
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• 2009

This study investigated the relationships of the self-regulated learning strategies used in both science and English classes and motivation to academic performance of science-gifted high school students. Participants of this study were 144 freshmen of Korea Science Academy It was found out that the use of self-regulation learning strategies and motivation exerts differential influence on the academic performance of science-gifted students, depending on the subjects they study. Results showed that they used more vigorously in science class those self-regulated strategies which consist of cognition, metacognition, and resource management strategies than in English class. In addition, their motivation level in science class was significantly higher than that in English class. Self-regulated strategies did not explain any variance in physics GPA. Task value among the motivation variables accounted for 2 percent of variance in physics GPA. Metacognition and time and study environment variables explained 8 percent and 15 percent of variance in English GPA, respectively. Self-efficacy in motivation accounted for 30 percent of variance in English GPA, These results were discussed in the light of instruction for science-gifted high students.

• Journal of Korean Biological Nursing Science
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• v.25 no.2
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• pp.87-94
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• 2023

Purpose: The purpose of this study was to identify the impact of cognitive flexibility and e-learning digital literacy on the learning flow of nursing students who had experienced e-learning. Methods: The research design for this study was a descriptive survey using convenience sampling. Data were collected using online questionnaires completed by 134 nursing students in Andong city and Pocheon city. The data were analyzed using percentages, mean values, standard deviations, Pearson's correlation coefficients, and multiple regression with SPSS for Windows version 22.0. Results: Positive correlations were found between learning flow and e-learning digital literacy (r = .43, p < .001), between learning flow and cognitive flexibility (r = .52, p < .001), and between e-learning digital literacy and cognitive flexibility (r = .65, p < .001). In the multiple regression analysis, cognitive flexibility (β = .42, p < .001) was a significant predictor that explained 27.8% of variance in learning flow. Conclusion: The results of this study show that cognitive flexibility is a factor influencing learning flow in nursing students. Based on the results of the study, educational programs aiming to improve learning flow should include methods that improve cognitive flexibility.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.108-114
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• 1996

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.

• The Journal of Korean Academic Society of Nursing Education
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• v.6 no.2
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• pp.359-375
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• 2000

PBL can be defined as an active, self-directed and student-centered learning, and an opposite way of classroom teacher-centered learning which has been traditional role learning. PBL enables students think more efficiently and effectively when puzzling through the patient problems. The purpose of this study is to find out the perception of student nurse about PBL, the characteristics and the structure of the type for PBL. The research process is as follow : First, the researcher selected 35 statements for PBL with the content analysis of in depth interview and the literature review. Second, the researcher asks 38 student nurse to classify the statement cards. The result of the research is that the type of student nurse's PBL perception is divided into 4 types(Affirmative type, Negative type, Suspicious type, and Preferable type), and the explanative total variance is 44 percent. In relation to this, if PBL well combined and adapted in our traditional curriculum will change our nursing education in better direction.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.50-53
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• 2007

This paper presents face features detection and a new physiological neuro-fuzzy learning method by using two-dimensional variances based on variation of gray level and by learning for a statistical distribution of the detected face features. This paper reports a method to learn by not using partial face image but using global face image. Face detection process of this method is performed by describing differences of variance change between edge region and stationary region by gray-scale variation of global face having featured regions including nose, mouse, and couple of eyes. To process the learning stage, we use the input layer obtained by statistical distribution of the featured regions for performing the new physiological neuro-fuzzy algorithm.

• ETRI Journal
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• v.43 no.6
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• pp.1004-1012
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• 2021

The collaboration productively interacting between multi-agents has become an emerging issue in real-world applications. In reinforcement learning, multi-agent environments present challenges beyond tractable issues in single-agent settings. This collaborative environment has the following highly complex attributes: sparse rewards for task completion, limited communications between each other, and only partial observations. In particular, adjustments in an agent's action policy result in a nonstationary environment from the other agent's perspective, which causes high variance in the learned policies and prevents the direct use of reinforcement learning approaches. Unexpected social loafing caused by high dispersion makes it difficult for all agents to succeed in collaborative tasks. Therefore, we address a paradox caused by the social loafing to significantly reduce total returns after a certain timestep of multi-agent reinforcement learning. We further demonstrate that the collaborative paradox in multi-agent environments can be avoided by our proposed effective early stop method leveraging a metric for social loafing.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.4
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• pp.90-104
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• 2023

This study analyzes the perceptions of college students majoring in aviation services according to the field learning program conducted during their major studies in order to reflect the educational value and academic awareness of the experience of the experiential field learning program. A survey is conducted targeting college students who experience a field learning program conducted by the Aviation Service Department of J University, a four-year university in the Chungcheong region. ANOVA (one-way analysis of variance) is conducted to analyze differences in perceptions of field learning properties, learning satisfaction, academic self-efficacy, and intention to continue studying. Additionally, text mining is conducted using 'Voyant Tools' to analyze students' field trip logs regarding field trip learning program activities. I hope that the results will be used as evidence to build an efficient and systematic learning strategy for operating field learning programs.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.404-412
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• 2010

The purpose of this study is to suggest the basic data for lowering cyber university students' dropout rate and fostering continuous learning environment through understanding that cyber university student's private variance, an education institute variance and social variance have the impact on a student's determining dropout. For this, we selected students in A cyber university and carried out surveys for 500 students from April first to May 31st, 2009 using convenience sampling. We excluded answers whose results are considered to be insufficient or overlapped among answers of 336 students and used 304 answers in this study. We carried out logistics regression analysis using SPSS for Winow 15.0 for data analysis. First, it proved that individual interest variance affects the dropout. Second, it turned out that educational institute's environment variance has impact on the dropout. Third, it proved that social environment factor affects the dropout. Fourth, only individual variance among individual, an educational institute and social variance has meaningful impact on the dropout in terms of statistics.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.