• Korean Journal of Occupational Health Nursing
• /
• v.29 no.4
• /
• pp.354-362
• /
• 2020

Purpose: This study aimed to define and clarify learning transfer in nursing. Methods: This study used a hybrid model to analyze the concept of learning transfer in nursing through three phases. For the theoretical phase, learning transfer attributes were identified through a scoping literature review. In the fieldwork phase, in-depth focus group interviews were conducted to develop attributes. Purposive sampling was performed with ten participants(five nursing students, two nurses, three nursing faculty members). In the analysis phase, the attributes and final analysis of learning transfer in nursing were extracted and integrated from the previous two phases. Results: According to the analysis, learning transfer was represented in two dimensions with eight attributes. The development of competency dimension had three attributes: 1) theory acquisition, nursing skills, professional attitude, 2) integration, and 3) analysis competency. The competency change dimension had five attributes: 1) appropriateness in patient care, 2) proficiency in patient care, 3) satisfaction, 4) achievement, and 5) confidence. Conclusion: The concept analysis might provide a basic understanding of learning transfer, a development framework toward a measurement of nursing learning transfer and effective educational nursing strategies.

• Journal of Engineering Education Research
• /
• v.22 no.5
• /
• pp.13-19
• /
• 2019

It is important to encourage students who are having difficulty learning mathematics and majors due to lack of basic knowledge, and encourage them to improve their academic performance by focusing on and participating in class. The purpose of this study is to confirm the applicability of the Jigsaw Method by analyzing the change of the learner's academic achievement and the attitude of the learner's mathematics by applying the Jigsaw Model. During the four weeks, the Jigsaw Model was applied to 30 experimental students and the instructor-led lectures were given to 36 students in the comparative group. As a result of the study, it was confirmed that the average of the experimental groups applying the Jigsaw model was higher than the average of the comparative group in the lecture class. The results of the survey showed that the Jigsaw Model increased class concentration and participation, helped self - directed learning, and had high learning satisfaction.

• Journal of the Korean association of regional geographers
• /
• v.11 no.4
• /
• pp.523-535
• /
• 2005

This study is aimed at researching the applicability of teaching-learning models in highschool geography class by designing the models on the basis of geographical experience the learners go through everyday life. The procedures and results of the application of the models are as followed. First, the systematization of the teaching concepts should be preceded to internalize the learners cognitive development, that is, to systemize cognitive structure. The concrete learning points of geographical concepts from the units about Migration and Population Changes are systemized with 'migration' as a higher concept, 'moving type' as basic concept, 'moving factors' as the lower concept. Everyday geographical experiences the students can go through are surveyed. Second, as preparation for the geography class, hand-outs about family-moving history and the change of the family number were used as basic material for real class teaching activity, showing the learners' general concepts are very effective as basic units which can be easily understood and accessed to. Third, with the experimental class, the geography class should secure the flexibility on the teaching-learning process. The result of applying the newly developed teaching-learning model to actual geography classes was that experimental group had higher achievement rate than the compared group with general teaching-learning model applied to. The result of analyzing students' response of the new teaching-learning model was that the students were interested and satisfied emphatically and they showed positive response in regard to practical use of the contents. Here, it is noticeable that the new teaching-learning model causes the students to be interested. But it's also found that there's no big difference in improving the students' inquisitive mind.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.6
• /
• pp.639-645
• /
• 2018

This paper is intended to develop a decision model that can be applied to autonomous vehicles and autonomous mobile vehicles. The developed module has an independent configuration for application in various driving environments and is based on a platform for organically operating them. Each module is studied for decision making on lane changes and for securing safety through reinforcement learning using a deep learning technique. The autonomous mobile moving body operating to change the driving state has a characteristic where the next operation of the mobile body can be determined only if the definition of the speed determination model (according to its functions) and the lane change decision are correctly preceded. Also, if all the moving bodies traveling on a general road are equipped with an autonomous driving function, it is difficult to consider the factors that may occur between each mobile unit from unexpected environmental changes. Considering these factors, we applied the decision model to the platform and studied the lane change decision system for implementation of the platform. We studied the decision model using a modular learning method to reduce system complexity, to reduce the learning time, and to consider model replacement.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.157-157
• /
• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• Proceedings of the KIEE Conference
• /
• 2001.07a
• /
• pp.386-388
• /
• 2001

The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Besides, it is required the index that consolidate the price volatility experienced on the power exchange with gaming and strategic bidding by suppliers to increase profits. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads, the price-sensitive load model is needed. And the development of state-of-the-art technologies affects the electricity price, so the diffusion of high-efficient end-uses and these price affect load patterns. This paper shows the analysis on learning curves algorithms which is used to investigate the correlation of the end-uses' price and load patterns.

• Journal of The Korean Association For Science Education
• /
• v.17 no.3
• /
• pp.333-343
• /
• 1997

The purpose of the present study was to investigate an appropriate instructional model in order to remedy students' misconception. As hypotheses of this study, three instructional models, cognitive conflicting, hypothesis testing, and learning cycle models, on biological 'osmosis' concept were tested in 176 high school students. Results of the present study are as follows: 1. All groups used one of three instructional models showed a statistically significant improvement in conceptual change on the 'osmosis' concept between before and after the instruction. In addition, the three hypothesized instructional models were more effective in conceptual change than a traditional expository instruction. 2. There was a statistically significant difference among three experimental groups. Cognitive conflicting model and hypothesis testing model was more effective than learning cycle models. 3. An interviewing after instruction showed that students who had scientific concept on the 'osmosis' through the instruction could effectively apply the concept to other context more than students who had no scientific concept through instruction. The present study indicated that instructional model play an important role on students' conceptual change in science classroom. According to the result of this study, the instruction emphasizing students' active participation in class and scientific reasoning process is more appropriate to remedy misconception that the instruction using students' passive participation in class and expository teaching procedure. This study also indicated that students' concept acquired through instruction is one of important factors to apply it to other context.

• Knowledge Management Research
• /
• v.17 no.2
• /
• pp.205-229
• /
• 2016

Owing to the current economic downturn, one of the most important goals of the organizations who are actively involved in Information Technology Outsourcing (ITO) is the cost efficiency. We focus on supplier firm's domain understanding to make the cost efficiency; therefore, we examine how the disadvantages from lower domain knowledges affect outsourcing performance moderated by outsourcing ratio and knowledge change environments. That is, if clients can endure disadvantage from service providers' lower domain knowledge, they can achieve cost efficiency by choosing lower domain knowledge suppliers with less expensive cost. To examine performance gap depending on the environments, we applied 'A Learning Model of IT Outsourcing' which is suggested by previous literature. As a result, we suggest five strategies for clients to contract with suppliers which have lower domain knowledge: (1) Prepare the strategy to endure disadvantages from the early stage. (2) Make the strategy depending on outsourcing ratio. (3) Knowledge transfer between organizations is important. (4) Make a short-term contract if they do not have good environments for organizational learning. (5) Client's knowledge change environments are more important than those of supplier's. Finally, we offer various implications for clients and suppliers in IT outsourcing.

• Journal of KIISE:Software and Applications
• /
• v.26 no.9
• /
• pp.1125-1131
• /
• 1999

정보자원의 변화에 따라 학습의 유형도 점차 변화되고 있으며 그에 따라서 컴퓨터를 이용하는 학습 형태도 변화할 필요가 있다. 그것은 학생이 중심이 되는 학습형태이며 그 중의 하나가 문제해결학습형태이다. 컴퓨터와 정보통신기술을 이용하는 문제해결능력은 구체적인 수준에서의 컴퓨터 관련 지식과 기술에 대한 체계적인 조직이 필요하다. 본 연구에서는 정보소양의 단계를 문제인식, 전략수립, 정보수집, 정보가공, 정보출력, 반성단계로 제시하고 그에 따른 컴퓨터관련 지식과 기술을 조직하여 문제해결능력 신장을 위한 컴퓨터 교육과정의 한 모델을 제시하였다.Abstract Learning styles have got to vary gradually according to the change of information resources. Thus We need to change learning styles with a computer. It's a student-oriented learning types. One of them is called a problem solving learning type. The problem solving ability with a computer and information communication technology require the systematic organization of concrete knowledges and the technology about computer. In this study we found the steps of information literacy as problem cognition, strategies establish, information collection, information processing, information presentation, reflection and proposed a model of computer curriculum to improve the problem solving ability.

• International Journal of Highway Engineering
• /
• v.20 no.2
• /
• pp.127-135
• /
• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.