• Journal of the Korean School Mathematics Society
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• v.18 no.1
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• pp.123-148
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• 2015

After government released the measures to reduce private tutoring and to advance math education, the Education Ministry carrying out projects to narrow the gap of education using ICT of the agricultural, mountain and fishing villages with 'ICT Supporting Business for the rural communities'. EBS(Educational Broadcasting System) also has established a website for self-directed math learning called EBSmath and offers various and customized services. This study has been conducted on how smartifact-assisted learning on EBSmath provided by 'ICT Supporting Business for the rural communities' will affect self-directed math learning of students. In other words, the purpose of this study is to see if students of the farming villages with poor surroundings of education using ICT can acquire knowledge for themselves and organize it systematically, and then they can finally produce new knowledge while they learn through EBSmath.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.2
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• pp.29-48
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• 2019

The free learning semester system focuses on the activation of career education of middle school students as an educational policy that links career recognition in elementary school, career search in middle school, and career planning in high school. This system was fully implemented in 2016 and public libraries also provided various programs. This study analyzed the free learning semester system programs of public libraries in Busan city and demonstrated the contribution of local development. As a result, career and job search, career exploration and experience, and information literacy enhancement programs contributed to local knowledge culture, reading culture, learning culture, living culture and leisure culture. However, contribution of reading exhibitions, job experience, information literacy enhancement to the leisure culture and local economy were limited. Therefore, it is desirable that all libraries should add programs related to knowledge ecosystem structure, digital information gap, human healing, social environment issues, future job prospects, and provide information literacy programs.

• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.283-291
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• 2021

In this paper, we propose the development and application of a software education program for underachievers. The software education program for underachieving students was developed in consideration of the characteristics of learner's suffering from underachievement and the educational effects of software education, and is meaningful in that it proposes a plan to improve the learning gap in distance learning. Learners can acquire digital literacy and learning skills by solving structured tasks in the form of courseware, intelligent tutoring, debugging, and artificial intelligence learning models in educational programs. Based on the effects of software education, such as enhancing logical thinking ability and problem solving ability, this program provides opportunities to solve fusion tasks to underachievers. Based on this, it is expected that it can have a positive effect on the overall academic work.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.953-966
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• 2022

Since aerosols adversely affect human health, such as deteriorating air quality, quantitative observation of the distribution and characteristics of aerosols is essential. Recently, satellite-based Aerosol Optical Depth (AOD) data is used in various studies as periodic and quantitative information acquisition means on the global scale, but optical sensor-based satellite AOD images are missing in some areas with cloud conditions. In this study, we produced gap-free GeoKompsat 2A (GK-2A) Advanced Meteorological Imager (AMI) AOD hourly images after generating a Random Forest based gap-filling model using grid meteorological and geographic elements as input variables. The accuracy of the model is Mean Bias Error (MBE) of -0.002 and Root Mean Square Error (RMSE) of 0.145, which is higher than the target accuracy of the original data and considering that the target object is an atmospheric variable with Correlation Coefficient (CC) of 0.714, it is a model with sufficient explanatory power. The high temporal resolution of geostationary satellites is suitable for diurnal variation observation and is an important model for other research such as input for atmospheric correction, estimation of ground PM, analysis of small fires or pollutants.

• Journal of The Korean Association For Science Education
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• v.37 no.6
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• pp.1025-1035
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• 2017

The purposes of this study are to analyze the merits and limits of flipped learning by suggesting the ALP model for efficient application and to test the effects of the new ALP model. The process of new model and program development is based on ADDIE in this study. This study consists of two steps. First through literature research on the difficulties of the flipped learning, the elements are extracted to develop new model. Second, these elements were placed according to the teaching and learning flow, which resulted in the procedures. As a result, the ALP model was developed. The ALP model is a new model for applying teaching and learning methods for efficient application of the flipped learning. This model was applied to elementary science classes to test its effects in scientific communication skill. Interviews and cognitive survey were also conducted to collect additional information. The results of this study are as follows: There were various difficulties in flipped learning. Based on literature research results, the ALP model and the science programs for elementary students have been developed. The experimental group showed statistically meaningful improvement in scientific communication skill. The scientific communication skill has two subcategories: the forms and the types. According to the form analysis results, the experimental group showed a statistically meaningful improvement in the form of Table and Picture, but not in the form of Writing and Number. With the same reason given previously, this study confirmed that the application of ALP model improves the students' visual form communication skills such as Table and Picture better than reading form communication skills such as Writing and Number. According to the type analysis results, the experimental group showed a statistically meaningful improvement in "the scientific insistence" type, and "the justification" which is the sub element of "the scientific insistence" type. With this reason, this study suggests that the class applied ALP model gives students more time and opportunities to learn. Though the survey and interviews about the student's awareness of the class with applied the ALP model, this study showed that students actively exchanged their opinions in the class with applied ALP model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.392-412
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• 2018

Due to the semantic gap problem across different modalities, automatically retrieval from multimedia information still faces a main challenge. It is desirable to provide an effective joint model to bridge the gap and organize the relationships between them. In this work, we develop a deep image annotation and classification by fusing multi-modal semantic topics (DAC_mmst) model, which has the capacity for finding visual and non-visual topics by jointly modeling the image and loosely related text for deep image annotation while simultaneously learning and predicting the class label. More specifically, DAC_mmst depends on a non-parametric Bayesian model for estimating the best number of visual topics that can perfectly explain the image. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. The experimental results show our proposed DAC_mmst performs favorably in perplexity, image annotation and classification accuracy, comparing to several state-of-the-art methods.

• Journal of the Korean Society of Earth Science Education
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• v.7 no.2
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• pp.159-168
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• 2014

This study was designed to 10 questions as development of GAP program for ninety high school students(each student of 30 with achievement as high, medium, and low categories), it was analyzed the perception of Earth scientist. High school students have a positive perception about a course in Earth science, but they have lack of knowledge about Earth scientist as a career man, and they can't recognize Earth scientist as a career. A failure of learning of Earth science for Students with low level achievement leads to a negative perception about Earth scientist and disconnection to future career. School education should provide an opportunity to encounter Earth scientist for students and it is badly in need of effort to connect to the job training program.

• Journal of Korean Society of Rural Planning
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• v.22 no.2
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• pp.77-87
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• 2016

Targeting people returning to farming and also people preparing for returning to farming, this study analyzed differences in awareness of conflict factors and conflict management focusing on the conflicts experiencing in the process of their movement and settlement process in rural area. In the results, people preparing for returning to farming showed higher awareness of conflicts and also higher necessity of conflict management than people already returning to farming. Also, both groups preferred individual conflict management to structural conflict management. Based on the results like above, the implications can be summarized like below. First, it would be necessary to have programs informing possible conflicts in advance in the process of returning to farming and also relieving psychological anxiety by providing prior-learning to people preparing for returning to farming. Second, it would be necessary to have individual conflict management measures to establish mutual trust and to form community spirit through regular social gatherings between original residents and people returning to farming. Since the effect of conflict management can be maximized only when the structural and individual conflict managements are properly harmonized, it would be necessary to have the structural conflict management which is relatively felt difficult.