• 대한공업교육학회지
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• v.34 no.2
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• pp.267-285
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• 2009

The collaborative learning has been considered as an efficient teaching model and under the recent basic learning environment, even face-to-face classroom circumstance rapidly increases the courses of blended learning which utilize the merits of e-learning environment. Nonetheless, the study on the strategy for systematic blended learning is quite scarce. In this study, the survey was done for developing the blended learning strategy, based on the collaborative learning model at the face-to-face environment and judging the satisfaction on the courses which the model was applied to. The survey consists of demographic questions, satisfaction in the whole courses, satisfaction in the collaborative learning under the blended learning environment and satisfaction in the blended learning strategy and support tools applied to each step of the learning. The result of this study is as follows. First, in response to the question that the blended learning can complement the face-to-face classroom courses, the respondents represented average 4.09 at 5-point Likert scale. And to the question whether the collaborative learning is more efficient under the blended learning environment than the face-to-face classroom, the response corresponds to 4.06 scale on the average. Second, as for the satisfaction in the blended learning strategy and support tools applied to the each step of the blended learning, the satisfaction degree is analyzed as high as over 4.0 on the average toward all the questions. Third, regarding the support tools used for the blended learning strategy, the learners consider the tools as most helpful in order of chatting, team community, mail & note and archive. Lastly, I would like to suggest that the study result should be highly reflected in constructing the collaborative learning module of learning control system in the future.

• Asia pacific journal of information systems
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• v.31 no.1
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• pp.43-75
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• 2021

Clearly, the smartphone is increasingly playing a greater role in everyday life, thus providing opportunities to evaluate how well the use of the smartphone meets the requirements of undergraduate students in independent learning outside of a classroom setting. This study used the task-technology fit (TTF) model to explore the effectiveness of smartphone usage to interact with learning materials in independent learning outside of classrooms, the need for smartphone support, and the fit of devices to tasks as well as performance. First, the study used interviews, observation, and survey data to identify what are the most important constructs of smartphones that stimulate students to interact with learning materials in independent learning outside of classrooms. Based on the findings from the exploratory study and Task Technology Fit theory, we postulated the Navigation design, Ergonomic design, Content support, and Capacity as the essential dimension of the smartphone construct. Then, we proposed a research model and empirically tested hypotheses with the structural model analysis. The results reveal a significant positive impact of task and technology on TTF for smartphone usage to interact with learning materials in independent learning outside of classrooms; it also confirmed the TTF and performance have a direct effect on actual use.

• Journal of Distribution Science
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• v.15 no.8
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• pp.65-73
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• 2017

Purpose - Although e-learning has this advantage, currently many organizations have failed to recognize the necessity for basic e-learning educational training. It follows that practitioners working in the above organizations face the difficulty of having to find educational training processes of boosting their capabilities by themselves, rather than being able to utilize the educational training processes offered by e-learning. So of their own accord, learners have considered the necessity of information relating to being able to choose between high quality educational training processes. The purpose of this study is to propose opening e-learning content information for enabling an efficient choice of learning processes related to e-learning. Research design, data, and methodology - To pinpoint the items of e-learning content information, the study was initiated according to the following process. First, information relating to e-learning content (offered on e-learning websites) was researched. Second, based on the items of information which emerged from the research, selection and validity verification took place with 5 e-learning specialists as the subjects. Third, the opinions of adult learners at K University were collated relating to the items of information which emerged from the research. Results - The e-learning content information was comprised of 16 items in order to improve the choosing process for learner's e-learning contents. The analysis results showed that when learners were choosing e-learning processes, the most highly considered item was 'mobile support' (4.35). Following this (in order) were 'tuition fees' (4.30), 'certificate issuing' (4.23), and 'awareness of educational institution' (4.18). The least considered items were 'recruiting learners' (3.01) and 'tutor support' (3.18). Conclusions - The 16 items of e-learning content information in this study, were deemed to be helpful to learners in providing them with a choice of desirable e-learning process when this process was offered to them. Following this, there is a need for service institutions offering e-learning processes to make public the information suggested by this study. Research into educational methods additionally points to a necessity for not only e-learning forms, but also offline educational methods and a combination of blended learning to be offered and run parallel to e-learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.332-340
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• 2019

This descriptive research study was undertaken to identify the degree of learning motivation, transfer climate, learning self-efficacy, and transfer motivation, and to correlate the variables, in nursing students receiving simulation-based education. The subjects of this study were 4th grade nursing students who completed a simulation course at a nursing university; data collected using the self-report questionnaire were analyzed using the SPSS 21.0 program. Our results indicate high values of learning motivation, transfer climate (including the lower variables supervisor's support, peer's support, and transfer opportunity), learning self-efficacy, and transfer motivation. Learning motivation, learning self-efficacy, and transfer motivation significantly differed with respect to social motivation for entering school (Z=6.04, p=0.049; Z=6.92, p=0.031; Z=9.16, p=0.010, respectively) and major satisfaction (Z=8.55, p=0.036; Z=12.55, p=0.006; Z=13.47, p=0.004, respectively). All these variables were positively correlated, especially transfer motivation with learning motivation, supervisor's support, peer's support, transfer opportunity, and learning self-efficacy. Taken together, the results of this study indicate a need to develop an effective simulation-based education program to encourage transfer motivation, as well as follow-up studies that verify the causal relationship between transfer motivation and related variables.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.21-27
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• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3417-3421
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• 2010

USN, all things are given to the computing and networking capabilities and by enabling the best service through awareness of environment and situation is a technology to improve the convenience and safety of human life. In this paper, we develop learning support system based on embedded system using USN technology which collect learner's learning environment based on real time and makes the best learning environment. In addition, simulations of these systems to improve the learners' learning efficiency was identified.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.11 no.3
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• pp.292-298
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• 2004

Purpose: this study was to describe patient learning needs and the relationship between health promoting behavior and health concept with women with disabilities. Methods: A descriptive survey design was used and the SPSS 11.0 program was used for data analysis, which included t-test, ANOVA and Pearson correlation coefficients. The women (n=50) were in-patients in a rehabilitation center. Results: The study results indicate that they had high levels of patient learning needs and the most important information for patient learning needs was support and care. Patient learning need was correlated with health promoting behavior. Conclusions: The findings of this study give useful information to construct further studies in educational programs and rehabilitation nursing care and to support a healthcare system for women with disabilities.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.66-72
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• 2006

In face recognition, learning speed of face is very important since the system should be trained again whenever the size of dataset increases. In existing methods, training time increases rapidly with the increase of data, which leads to the difficulty of training with a large dataset. To overcome this problem, we propose SVDD (Support Vector Domain Description)-based learning method that can learn a dataset of face rapidly and incrementally. In experimental results, we show that the training speed of the proposed method is much faster than those of other methods. Moreover, it is shown that our face recognition system can improve the accuracy gradually by learning faces incrementally at real environments with illumination changes.

• Educational Technology International
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• v.22 no.2
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• pp.199-225
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• 2021

The aim of the current study was to suggest priorities needed to be considered by university instructors when designing online learning. Based on three types of interactions (learner-content, learner-instructor, and learner-learner interactions) for effective online learning (Moore, 1989), draft questionnaires representing each type of interaction were written. After examining content validity by two Ph.D. experts, the survey was constructed with an Importance-Performance Analysis (IPA) form. Data of 133 university instructors were collected online. Results showed that support for designing learner-learner interaction was the priority for improving online learning. In terms of learner-instructor interaction, instructors needed to provide social-emotional support to learners so that learners could have a sense of belonging. For learner-instructor interaction, supporting instructors to monitor the level of understanding was the most highly demanding strategy for online learning. Limitations and suggestions for further studies were discussed.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.225-232
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• 2023

Owing to advancements in intelligent transportation systems (ITS) and artificial-intelligence technologies, various machine-learning models can be employed to simulate and predict the number of traffic accidents under different weather conditions. Furthermore, we can analyze the relationship between weather and traffic accidents, allowing us to assess whether the current weather conditions are suitable for travel, which can significantly reduce the risk of traffic accidents. In this study, we analyzed 30000 traffic flow data points collected by traffic cameras at nearby intersections in Washington, D.C., USA from October 2012 to May 2017, using Pearson's heat map. We then predicted, analyzed, and compared the performance of the correlation between continuous features by applying several machine-learning algorithms commonly used in ITS, including random forest, decision tree, gradient-boosting regression, and support vector regression. The experimental results indicated that the gradient-boosting regression machine-learning model had the best performance.