• 영재교육연구
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• 제23권2호
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• pp.177-191
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• 2013

본 연구는 초등 영재아와 일반아의 학습양식, 자기조절학습전략 및 학습몰입 간의 차이와 학습양식과 자기조절학습전략이 학습몰입에 미치는 영향을 비교분석하고자 하였다. 초등학교 6학년 영재아 118명과 일반아 124명을 대상으로 연구한 결과 첫째, 영재아는 일반아보다 독립적, 경쟁적, 참여적 학습양식을 가졌으며, 개인적, 행동적, 환경적 자기조절학습전략을 더 많이 사용하였고, 학습몰입의 정도가 높았다. 둘째, 영재아와 일반아의 독립 및 참여적 학습양식은 자기조절학습전략과 학습몰입에 영향을 미쳤다. 셋째, 영재아의 학습몰입은 학습양식이 참여적이고 독립적일수록, 행동적, 개인적 자기조절학습전략을 많이 사용할수록 높았고, 일반아의 학습몰입은 학습양식이 참여적일수록, 개인적, 환경적 자기조절학습전략을 많이 사용할수록 높았다.

• 정보교육학회논문지
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• 제12권1호
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• pp.89-97
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• 2008

제 7차 초등학교 과학과 교육과정에서는 특히 탐구학습을 강조하고 있다. 그러나 단위 시간과 학습자료의 부족이라는 현실적인 이유 때문에 학교 교육 현장에서는 실제적인 적용이 어렵다. 본 연구에서는 이러한 문제점을 해결하기 위하여 웹기반 발견학습 시스템을 설계 구현하고 그 효과성을 검증하는데 목적이 있다. 검증 과정으로 초등학교 4학년 과학과의 용수철 늘이기 단원을 대상으로 일반적인 발견학습 모형을 적용한 비교집단과 웹기반 발견학습 시스템으로 수업을 받은 실험집단의 학업성취도에 있어 유의미한 차이가 있는지 조사하였다. 적용 결과, 웹기반 발견학습 시스템으로 수업을 한 경우, 전통적인 발견학습 모형 그대로 적용한 경우보다 학업 성취도가 높았다.

• International Journal of Advanced Culture Technology
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• 제9권1호
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• pp.190-195
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• 2021

Currently, one of the fields leading the 4th industrial revolution is the image recognition field of artificial intelligence, which is showing good results in many fields. In this paper, using is a YOLO V2 model, which is one of the image recognition models, we intend to classify and select into three types according to the characteristics of fruits. To this end, it was designed to proceed the number of iterations of learning 9000 counts based on 640 mandarin image data of 3 classes. For model evaluation, normal, rotten, and unripe mandarin oranges were used based on images. We as a result of the experiment, the accuracy of the learning model was different depending on the number of learning. Normal mandarin oranges showed the highest at 60.5% in 9000 repetition learning, and unripe mandarin oranges also showed the highest at 61.8% in 9000 repetition learning. Lastly, rotten tangerines showed the highest accuracy at 86.0% in 7000 iterations. It will be very helpful if the results of this study are used for fruit farms in rural areas where labor is scarce.

• Biomolecules & Therapeutics
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• 제30권2호
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• pp.179-183
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• 2022

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.

• 대한지역사회영양학회지
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• 제4권4호
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• pp.504-511
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• 1999

The purpose of this survey was to investigate the relationship among obese children, dietary intake and environmental factors. Therefore, this survey compared obese children with normal children on dietary intake and environmental factors. The survey were 110 obese children and 110 normal children whose age, height, and sex were same as the obese children of 21 elementary school in Inchon. The statistical analysis of data was completed using SAS program. The results were summarized as follows : 1) The student's obesity was related to parent's obesity and number of their siblings. There were significant differences between obese groups and normal group for these two factors. 2) Meal time of the obese group showed more irregularity than that of the normal group. Otherwise, the normal group were more "piclity" about special food than the obese group(p<0.05). Also obese children showed unconsious eating while reading or watching TV(p<0.01). The normal group attended physical education class more eagerily the obese group(p<0.001). There was no significant difference between obese children and normal children for learning habits. 3) Obese children shoed higher intake of nutrients compared to normal children. Among all the nutrients, minerals and vitamins showed significant differences. Therefore, further study on obese children and their intake of minerals and vitamin is needed. Also, in order to prevent factors which influence obesity, nutrition education at home as well as school was needed.as needed.

• 한국포장학회지
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• 제26권3호
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• pp.139-145
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• 2020

The purpose of this study is to share the experience of online education with departments related to the servic industry, such as distribution and logistics, in the New Normal Age of COVID-19 pandemics. The graduation presentation project, which is the capstone design subject and the most urgent subject for face-to-face classes, was selected as the online education subject of this study. The results of this study are as follows : First, several online class tools, such as Zoom, Microsoft Teams, and Google Meet, have also been shown to be applicable to capstone design subjects such as graduation presentation projects. Second, it is essential to reorganize the curriculum to enhance students' ability to practice and utilize online contents. Third, continuous education and training are needed to make easy use of the aforementioned online teaching tools. Meanwhile, fourth, further research is needed to solve the learning effects caused by online learning, and difficulties in communication.

• PNF and Movement
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• 제12권2호
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• pp.81-88
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• 2014

Purpose: The purpose of this study is to examine the needs of prior field learning and the academic achievement of field experience learning in a college. Methods: This study was performed from May 1 to October 30, and students were given questionnaire. The research questionnaire as follows: (1) to investigate the academic achievement after field experience learning, (2) to verify the needs of field experience learning. A statistical analysis was performed using SPSS 17.0 for window version. Results: The results was as follows : First, satisfaction of field learning had scored good(47.2%) in lesson goal, good(51.8%) in acquisition of knowledge and techniques, good(51.0%) in preparation of study and good(45.9%) in association. Second, curriculum of field learning had scored normal(35.5%) in prior education, good(47.4%) in composition, good(50.8%) in guidance and good(47.2%) in contents. Third, curriculum of field learning had scored good(44.6%) in duration, good(46.1%) in numbers, good(51.3%) in convenience and normal(38.1%) in means of transportation. Forth, needs of field learning had scored good(46.6%) in field learning of practicum, good(48.2%) in field learning of theory subject, 3-4 times(42.0%) in frequency of field learning and 2hours(57.3%) in a field learning hour. Conclusion: These findings suggest that college student's thinking of field experience learning is positive. Field experience learning provided that college students have directly an opportunity of gaining valuable experience to feel the field.

• 한국축산식품학회지
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• 제29권2호
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• pp.278-282
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• 2009

Previous studies indicate that BF-7 enhances learning and memory in normal and elderly individuals. Here, we evaluated whether milk containing BF-7 (BF-7 milk) could improve the brain function, with thirty normal university students $(21{\pm}1.2 years)$. Two versions of the Paced Auditory Serial Addition Test were used under double-blinded conditions to measure the efficacy of BF-7 milk on learning and memory, especially working memory and attention, and on mathematical ability. As a result, BF-7 milk improved the accuracy of the task more than 3-fold. Furthermore, BF-7 milk protected cultured neuronal cells from 3-hydroxykynurenine, a normal endogenous brain stress agent. These results indicate that BF-7 milk enhances memory, attention and mathematical ability in normal persons.

• 인간식물환경학회지
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• 제21권6호
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• pp.523-532
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• 2018

In order to actively apply horticultural therapy to vocational education base schools, this study applied the learning theories based on behaviorism, cognitivism, and constructivism to improve the vocational skills of high school students with intellectual disabilities. The horticultural therapy program was carried out in total 12 sessions with three groups composed of 20 students each: 20 in the control group, 20 in the experimental group with a normal horticultural program, and 20 in the experimental group with a horticultural therapy program which applied the learning theories based on behaviorism, cognitivism, and constructivism. The results were analyzed with SPSS 12.0 using the Interpersonal Negotiation Strategies evaluation tool. The study found that in terms of interpersonal relationship, there was no significant difference between the pre and post tests within each group. However, in the post tests among the three groups, there was no significant difference between control group and experimental group with normal horticultural program (p=.82), but there was a significant difference between the control group and experimental group with the horticultural therapy program applying the learning theories based on behaviorism, cognitivism (p=.03). This proved that the horticultural therapy program applying the learning theories based on behaviorism, cognitivism is effective for improving interpersonal relationship. Ultimately the program is expected to be continuously, and systematically improved and applied as a learning method contributing to developing the basic vocational skills of persons with intellectual disabilities.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.506-515
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• 2023

This study is to find a method to learn engine sound after the start-up of a diesel generator installed in nuclear power plant with an unsupervised deep learning algorithm (CNN autoencoder) and a new method to predict the failure of a diesel generator using it. In order to learn the sound of a diesel generator with a deep learning algorithm, sound data recorded before and after the start-up of two diesel generators was used. The sound data of 20 min and 2 h were cut into 7 s, and the split sound was converted into a spectrogram image. 1200 and 7200 spectrogram images were created from sound data of 20 min and 2 h, respectively. Using two different deep learning algorithms (CNN autoencoder and binary classification), it was investigated whether the diesel generator post-start sounds were learned as normal. It was possible to accurately determine the post-start sounds as normal and the pre-start sounds as abnormal. It was also confirmed that the deep learning algorithm could detect the virtual abnormal sounds created by mixing the unusual sounds with the post-start sounds. This study showed that the unsupervised anomaly detection algorithm has a good accuracy increased about 3% with comparing to the binary classification algorithm.