• Journal of Korean Academy of Nursing
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• v.39 no.2
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• pp.249-258
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• 2009

Purpose: The purpose of this study was to develop a multimedia learning program for patients with diabetes mellitus (DM) diet education using standardized patients and to examine the effects of the program on educational skills, communication skills, DM diet knowledge and learning satisfaction. Methods: The study employed a randomized control posttest non-synchronized design. The participants were 108 third year nursing students (52 experimental group, 56 control group) at K university in Seoul, Korea. The experimental group had regular lectures and the multimedia learning program for DM diet education using standardized patients while the control group had regular lectures only. The DM educational skills were measured by trained research assistants. Results: The students who received the multimedia learning program scored higher for DM diet educational skills, communication skills and DM diet knowledge compared to the control group. Learning satisfaction of the experimental group was higher than the control group, but statistically insignificant. Conclusion: Clinical competency was improved for students receiving the multimedia learning program for DM diet education using standardized patients, but there was no statistically significant effect on learning satisfaction. In the nursing education system there is a need to develop and apply more multimedia materials for education and to use standardized patients effectively.

• Journal of Broadcast Engineering
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• v.25 no.7
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• pp.1081-1094
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• 2020

With the recent development of deep learning, most computer vision-related tasks are being solved with deep learning-based network technologies such as CNN and RNN. Computer vision tasks such as object detection or object segmentation use intermediate features extracted from the same backbone such as Resnet or FPN for training and inference for object detection and segmentation. In this paper, an experiment was conducted to find out the compression efficiency and the effect of encoding on task inference performance when the features extracted in the intermediate stage of CNN are encoded. The feature map that combines the features of 256 channels into one image and the original image were encoded in HEVC to compare and analyze the inference performance for object detection and segmentation. Since the intermediate feature map encodes the five levels of feature maps (P2 to P6), the image size and resolution are increased compared to the original image. However, when the degree of compression is weakened, the use of feature maps yields similar or better inference results to the inference performance of the original image.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• Journal of Korean Home Economics Education Association
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• v.32 no.2
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• pp.99-115
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• 2020

The purpose of this study is to analyze the relationship between home economics curriculum and media literacy education. For this purpose, 12 kinds = types of learning materials for middle school 'Technology·Home Economics 2' textbooks were analyzed. After selecting 'Media Literacy Performance Goals(MLPG)' as the basis for analysis, the distribution of media data and reflection of MLPG were analyzed by frequency and content analysis. The results of this study are as follows. First, 39.6% of the learning materials using media materials out of the total learning materials of 12 textbooks, and there were differences in the frequency and weight of learning materials using media materials by publishers. Depending on the type of media, 68.3% of 'printing', 16.7% of 'images, video', 13.5% of 'digital', and 86.5% of the use of unidirectional media. Second, there was a difference in frequency and weight of learning materials reflecting the MLPG by publishers, and it was necessary to supplement the learning content to improve overall media literacy. Among the MLPG reflected in the learning materials, 'meaning and communication' was the most reflected performance goal, with 58.8%, but there was no two-way communication through the media. Based on the results of these textbook analysis, MLPG in Home Economics are revised as follows. 'Understanding the meaning and self-expression', 'Communication and social participation', 'Use of responsible media', 'Appreciation and enjoyment', 'Use of media technology', 'Information search and selection', 'Creation and production', 'Critical understanding and evaluation'.

• Journal of Educational Research in Mathematics
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• v.20 no.4
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• pp.459-476
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• 2010

The purpose of this study is to suggest a new direction in using LOGO as a gifted education program and to seek an effective approach for LOGO teaching and learning, by analyzing the strategic thinking of mathematically gifted elementary students. This research is exploratory and inquisitive qualitative inquiry, involving observations and analyses of the LOGO Project learning process. Four elementary students were selected and over 12 periods utilizing LOGO programming, data were collected, including screen captures from real learning situations, audio recordings, observation data from lessons involving experiments, and interviews with students. The findings from this research are as follows: First, in LOGO Project Learning, the mathematically gifted elementary students were found to utilize such strategic ways of thinking as inferential thinking in use of prior knowledge and thinking procedures, generalization in use of variables, integrated thinking in use of the integration of various commands, critical thinking involving evaluation of prior commands for problem-solving, progressive thinking involving understanding, and applying the current situation with new viewpoints, and flexible thinking involving the devising of various problem solving skills. Second, the students' debugging in LOGO programming included comparing and constrasting grammatical information of commands, graphic and procedures according to programming types and students' abilities, analytical thinking by breaking down procedures, geometry-analysis reasoning involving analyzing diagrams with errors, visualizing diagrams drawn following procedures, and the empirical reasoning on the relationships between the whole and specifics. In conclusion, the LOGO Project Learning was found to be a program for gifted students set apart from other programs, and an effective way to promote gifted students' higher-level thinking abilities.

• Communications of Mathematical Education
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• v.34 no.3
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• pp.215-234
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• 2020

With the advent of the AI, the need to use AI in the field of education is widely recognized. The purpose of this study is to shed light on how prospective mathematics teachers perceive the need for AI and the role of teachers in future mathematics education. As a result, with regard to teaching, prospective teachers recognized that the use of AI in school mathematics is a demand of a new era, that various types of lesson can be implemented, and that accurate knowledge and information can be delivered. On the other hand, they recognized that AI has limitations in having cognitive and emotional interactions with students. As for mathematics learning, the prospective teachers recognized that AI can provide individualized learning, be used for supplementary learning outside of school, and stimulate students' interest in learning. However, they also said that learning through AI could undermine students' ability to think on their own. With regard to assessment, the prospective teachers recognized that AI is objective, fair and can reduce teachers' workload, but they also said that AI has limitations in evaluating students' abilities in constructed-response items and in process-focused assessment. The roles of teachers that the prospective teachers think were to conduct a lesson, emotional interaction, unstructured assessment, and counseling, and those of AI were individualized learning, rote learning, structured assessment, and administrative works.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.105-120
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• 2019

The World Health Organization (WHO) and other government agencies aroundthe world have warned against antibiotic-resistant bacteria due to abuse of antibiotics and are strengthening their care and monitoring to prevent infection. However, it is highly necessary to develop an expeditious and accurate prediction and estimating method for preemptive measures. Because it takes several days to cultivate the infecting bacteria to identify the infection, quarantine and contact are not effective to prevent spread of infection. In this study, the disease diagnosis and antibiotic prescriptions included in Electronic Health Records were embedded through neural embedding model and matrix factorization, and deep learning based classification predictive model was proposed. The f1-score of the deep learning model increased from 0.525 to 0.617when embedding information on disease and antibiotics, which are the main causes of antibiotic resistance, added to the patient's basic information and hospital use information. And deep learning model outperformed the traditional machine hospital use information. And deep learning model outperformed the traditional machine learning models.As a result of analyzing the characteristics of antibiotic resistant patients, resistant patients were more likely to use antibiotics in J01 than nonresistant patients who were diagnosed with the same diseases and were prescribed 6.3 times more than DDD.

• Journal of The Korean Association of Information Education
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• v.6 no.3
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• pp.317-327
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• 2002

The need to support the ICT-using teaching and learning at elementary and secondary schools has led to various digital content service systems. The systems are designed to target the teachers and the students as the major users. The problems involved in them is that they do not provide such services as the integrated search and the systematic use of interface in terms of actual users' use of teaching and learning digital content. It's because they have been created at demands at each time. In an attempt to solve this problem, this study set out to suggest the integrated meta-data items of a teaching and learning digital content, which reflects the Dublin Core Education, the international meta-data standard. It also aimed to design an entity-relationship model to realize the digital content. The results of the integrated meta-data and the entity-relationship model will be utilized as a basic research to help the users to search for various teaching and learning digital contents on an integrated basis and to realize a consistent user interface. Furthermore, they are expected to contribute to the development a service system the teachers and the students can make better use of.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.47-56
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• 2020

The purpose of this study is to comprehensively analyze elementary school teachers' perceptions of the use of artificial intelligence in education. Recently, interest in the use of artificial intelligence has increased in the field of education. However, there is a lack of research on the perceptions of elementary school teachers using AI in education. Using descriptive statistics, multiple linear regression analysis, and semantic differential meaning scale, 69 elementary school teachers' perceptions of using AI in education were analyzed. As a results, artificial intelligence technology was perceived as most suitable method for assisting activities in class and for problem-based learning. Factors which influence the use of AI in education were learning contents, learning materials, and AI tools. AI in education had the features of personalized learning, promoting students' participation, and provoking students' interest. Further, instructional strategies or models that enable optimized educational operation should be developed.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.41-49
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• 2019

This study aims to investigate the awareness and status of smart attendance systems in the professors and college students who directly use an electronic smart system, a learning management system utilizing IT and to propose a plan for improvement to increase the efficiency of the smart attendance system. As for the research method, this study conducted an online survey with 264 students at S. University to investigate the status of their use and awareness of the smart attendance system. As a result, first, the professors mostly were satisfied with the smart attendance system, and it would be necessary to improve learning ability and the function of self-management in connection with the learning management system. Second, the college students were dissatisfied with the user interface and speed of the smart attendance system, and it would be necessary to improve the delay time, login, update, and false attendance.