• 의학교육논단
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• 제17권1호
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• pp.33-38
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• 2015

During the education reform in 2009, the Catholic University of Korea College of Medicine (CUMC) adopted body systems as the basis for structuring basic medical education. After running the new program for 5 years, we need to evaluate the program by comparing it with nationwide standards. This study was designed to evaluate the coverage of our basic medical education program by comparing it with the assessment items of the medical licensing examination for physicians in the Republic of Korea. We built a relational database populated with 3,017 learning outcomes from all the courses on basic medical education. We tagged each learning outcome according to 2 criteria: 206 physician encounters and 9 outcome domains. A majority of the learning outcomes were in the domains of 'knowledge' and 'critical thinking'. In addition, we repeated the categorization process with 584 assessment items of the medical licensing examination in the Republic of Korea and compared them with the categorization results of the learning outcomes. Among the 206 physician encounters, we found that outcomes on family violence and sexual violence were missing in the learning outcomes of CUMC. Eighty-two physician encounters were associated with more than one outcome domain, and 96 physician encounters were covered in more than one course. Twenty-one physician encounters were repeated in 5 or more courses and 34 physician encounters had outcomes categorized into 3 or more domains. Thus, we showed that the 2-way categorization could be applied to the comparison and evaluation of two different education formats.

• 산업공학
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• 제19권4호
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• pp.324-332
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• 2006

It is believed that each learner has a preferred method to acquire and manage knowledge according to her/his learning style which influences learning achievement directly. The purpose of this paper is to statistically analyze relationships among individual learning styles, tutoring functions, and learning achievement in an e-learning environment. 524 survey results from participants of enterprise e-learning classes are classified into total group and superior group. T-Test and ANOVA analyses are carried between learning style and learning achievement and between learning style and preferred tutoring functions. The analysis results show that individual learning styles do not contribute to learning achievement while they are strongly related to preferences for some of tutoring functions. These results can be used to identify limitation of current e-learning practice and design better e-learning systems, especially, supporting appropriate tutoring functions for different types of learners.

• 대한간호학회지
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• 제35권6호
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• pp.1113-1124
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• 2005

Purpose: The purpose of this study was to develop an e-Learning program about medication for nurses to enhance nurses' medication performance ability and to analyze learners' responses after studying with this program. Method: For the development of the e-Learning program, the NBISD(Network Based Instructional Systems Design) model, suggested by Jung(I999) was applied as a basic model and the instruction design theory of Gagne & Briggs(1979) and ARCS theory of Keller(I983) were applied. After the operation of this program for one month to 34 new nurses, learners' responses were analyzed. Result: Learners' knowledge of medication was greatly improved after this program. In addition learners' satisfaction with the overall education program, help in field applicability, ease of screen shift and exploration, and tutor activities were high and the contents were regarded suitable for e-Learning. Many things were advantageous such as easy accessibility, easy understandability with pictures and flash animation, practical cases and feedback from a tutor. Provision of a supplementary handout and improvement of a tight time schedule were pointed out as things to be improved. Conclusion: This e-Learning program can be used effectively for medication education for registered nurses, student nurses, and new nurses.

• 로봇학회논문지
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• 제14권4호
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• 공학교육연구
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• 제24권2호
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• pp.3-11
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• 2021

In the COVID-19 era, it was implemented to be used as a basic material for setting the direction of learning support and student guidance for university institutions and professors who are experiencing confusion. The purpose of this study is to compare the actual status of daily-routines of COVID-19 period, general semester period, and vacation period, and to examine whether there is a difference between the period of general semester and COVID-19 period, and whether there is a difference in daily use of COVID-19 period depending on grade. For this reason, a questionnaire survey was conducted from April 23 to 29, 2020, targeting students of University A, which is a small-scale technical centered university in the region, and 754 students answered. As a result of the study, first of all, when we looked at the trends in the use of daily-routines by period of general semester, vacation period, and COVID-19 period, the trends of the general semester period and COVID-19 period were similar in the areas of learning and self-development. Second, there were statistically significant differences in sleep, relaxation, learning and other areas between the period of the general semester and the duration of COVID-19. Third, there were statistically significant differences over grade in relaxation, learning, development, and other areas.

• 공학교육연구
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• 제25권6호
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• pp.3-13
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• 2022

This study aims to develop and implement extra-curricular program applying project-based learning (PBL) and to find out its effectiveness. The research was conducted from March 2021 to December 2021 according to the ADDIE model. Surveys, t-test and descriptive statistics were also conducted. The results of the study are as follows. First, project-based learning was applied to the extra-curricular program by reflecting the characteristics of students of University A and the requirements of the person in charge of the teaching and learning center. Second, project tasks related to education of universities were proposed. Third, the programs were designed as three common courses and four consulting courses. Fourth, the program was conducted with 32 students for two months. Fifth, the post-test results of problem-solving skills rose to 0.9 points compared to the pre-test but there was no significant difference, while the post-test results of communication skills were 0.5 points lower than before and statistically significant. Sixth, the satisfaction survey result was high with a rating of 4.59. Lastly, educational implications are also discussed.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2023년도 춘계학술대회
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• pp.160-161
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• 2023

현재 선박의 항해계획은 항해사의 지식과 경험적인 방법에 의존하고 있다. 그러나 최근에는 선박 자율운항기술이 발전됨에 따라, 항해계획의 자동화 기술도 여러 가지 방법으로 연구되고 있다. 본 연구에서는 강화학습 기법 중 하나인 Q-러닝을 기반으로 선박 최적 항해 경로를 생성하고자 한다. 강화학습은 다양한 상황에 대한 경험을 학습하고, 이를 기반으로 최적의 결정을 내리는 방식으로 적용된다.

• ETRI Journal
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• 제22권3호
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• pp.10-18
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• 2000

Although an n-th order cross-entropy (nCE) error function resolves the incorrect saturation problem of conventional error backpropagation (EBP) algorithm, performance of multilayer perceptrons (MLPs) trained using the nCE function depends heavily on the order of nCE. In this paper, we propose an adaptive learning rate to markedly reduce the sensitivity of MLP performance to the order of nCE. Additionally, we propose to limit error signal values at out-put nodes for stable learning with the adaptive learning rate. Through simulations of handwritten digit recognition and isolated-word recognition tasks, it was verified that the proposed method successfully reduced the performance dependency of MLPs on the nCE order while maintaining advantages of the nCE function.

• 한국환경교육학회지:환경교육
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• 제12권1호
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• pp.365-377
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• 1999

The concept of environmental education contains from teaching environmental pollutions and the importance of environment to the ecological relationship between human and the nature. The ultimate goal of environmental eduaction, however, is to build the environmental-friendly and responsible behavoir. One of the best way to achieve this goal is ecological learning centers, where students can observe and analyse threes, plants and animals, and they learn the principle of the environmental succession with feeling and understanding. Students internalize environmental awareness through experiencing the nature. In this paper, we would like to introduce the diverse types of ecological learning centers in germany and their programs focusing on the ecocenters in berlin, with an intention of adopting practical programs in korean school system.

• 한국정보기술학회 영문논문지
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• 제10권2호
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• pp.61-72
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• 2020

Recently, researches that are using deep learning technology in various fields are being conducted. The fields include geographic map processing. In this paper, I propose a method to infer where the map area included in the image is. The proposed method generates and learns images including a map, detects map areas from input images, extracts character strings belonging to those map areas, and converts the extracted character strings into coordinates through geocoding to infer the coordinates of the input image. Faster R-CNN was used for learning and map detection. In the experiment, the difference between the center coordinate of the map on the test image and the center coordinate of the detected map is calculated. The median value of the results of the experiment is 0.00158 for longitude and 0.00090 for latitude. In terms of distance, the difference is 141m in the east-west direction and 100m in the north-south direction.