• Journal of the Korea Institute of Building Construction
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• v.19 no.5
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• pp.457-465
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• 2019

Technology and society have undergone continued progress and improvement. Thus, constant changes are required for university education, which then directs social development. This study analyzed the current status of educational innovations in the field of building construction at domestic universities in South Korea. The major findings of this study are as follows. Lectures on educational innovations have been introduced in the field of building construction at domestic universities. Five out of 50 universities were found to offer lectures on innovation. Notable innovative teaching methods being introduced were team-based learning and flipped learning. The biggest difference between innovative and traditional teaching methods was whether to encourage students to perform self-directed learning. In this manner, there were also differences in evaluation methods, weekly lecture schedules and learning support tools. Thus it is determined that continuous research and efforts for innovation in university education are necessary to respond to the ever-present changes in society.

• Journal of the Daesoon Academy of Sciences
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• v.34
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• pp.141-169
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• 2020

This study focuses on the interpretation and transformation of "The Great Learning" within the Korean new religion, Daesoon Jinrihoe. Joseon Dynasty Korea was a member of the Chinese Character Cultural Sphere in East Asia. The examination and recruitment system of the Yuan Dynasty influenced the Joseon Dynasty for a long historical period. Zhu Xi's (朱熹) version of The Four Books were accepted and applied in imperial examinations during the Joseon Dynasty. The 18th century Confucian thinker, Jeong Yak-Yong (丁若鏞), overturned and rebuilt his own system for studying and interpreting The Four Books (四書學). Zhu Xi and Jeong Yak-Yong's systems of thought influenced Confucianism knowledge in that era. The historical figure deified as the Supreme God by Daesoon Jinrihoe, Kang Jeungsan (姜甑山), was trained in the study of The Four Books within that cultural and philosophical context, and this is especially evident in his interpretation and transmission of "The Great Learning." Kang Jeungsan regarding The Great Learning as deeply important. That text combined Confucian discourse on Principle, Mind, and Practice. In his interpretation, The Great Learning was also a medical and religious book that had holy and mysterious powers. In Mugeuk-do and Taegeuk-do (direct predecessors to Daesoon Jinrihoe), Jo Jeongsan interpreted the concept of Sincerity and Regularizing the Mind and incorporated them into doctrine as 'Sincerity, Respectfulness, and Faithfulness' and 'Guarding against Self-deception.' Park Wudang practiced and spread those doctrines to Korea, and Daesoon Jinrihoe devotees continue to follow those doctrines in present times.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.91-97
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• 2022

Recently, as per a representative education method to develop core capabilities (such as critical thinking, communication, collaboration, and creativity) problem based learning (PBL) has been widely adopted in universities. Two important features of PBL are 'collaboration between team members' and 'participation based self-directed learning'. These two features should be satisfied in online education, although it is difficult due to the limitation on space and time in the COVID-19 pandemic. This paper presents a new design of PBL class in Metaverse, based on improving the online PBL class operated in the previous semesters in the H university. In the proposed PBL class, students are able to display materials (e.g., image, pdf, video files) in 3D virtual space, that are related to problem solving. The 3D virtual space is called gallery in this paper. The concept of gallery allows for active participation of students. In addition, the gallery can be used as a tool for collaborative meeting or for final presentation. If possible, the new design of PBL class will be applied and its effectiveness will be analyzed.

• Journal of Biomedical Engineering Research
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• v.45 no.2
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• pp.90-94
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• 2024

Diagnosing wounds presents a significant challenge in clinical settings due to its complexity and the subjective assessments by clinicians. Wound deep learning algorithms quantitatively assess wounds, overcoming these challenges. However, a limitation in existing research is reliance on specific datasets. To address this limitation, we created a comprehensive dataset by combining open dataset with self-produced dataset to enhance clinical applicability. In the annotation process, machine learning based on Gradient Vector Flow (GVF) was utilized to improve objectivity and efficiency over time. Furthermore, the deep learning model was equipped U-net with residual blocks. Significant improvements were observed using the input dataset with images cropped to contain only the wound region of interest (ROI), as opposed to original sized dataset. As a result, the Dice score remarkably increased from 0.80 using the original dataset to 0.89 using the wound ROI crop dataset. This study highlights the need for diverse research using comprehensive datasets. In future study, we aim to further enhance and diversify our dataset to encompass different environments and ethnicities.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1061-1080
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• 2023

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.459-463
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• 2024

Frequent closures among self-employed individuals lead to national economic losses. Given the high closure rates in the restaurant industry, predicting changes in this sector is crucial for business survival. While research on factors affecting restaurant industry survival is active, studies predicting commercial district changes are lacking. Thus, this study focuses on forecasting such alterations, designing a deep learning model for Seoul's administrative district commercial district changes. It collects 2023 and 2022 second-quarter variables related to these changes, converting yearly fluctuations into percentages for augmentation. The proposed deep learning model aims to predict commercial district changes. Future policies, considering this study, could support restaurant industry growth and economic development.

• Journal of Korean Elementary Science Education
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• v.43 no.2
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• pp.252-268
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• 2024

This study developed a field trip program called "Planning a science exhibition" and explored elementary science-gifted students' perceptions of the program and learning experiences influencing them. To this end, 56 elementary science-gifted students in grades 4-6 from in an university-affiliated science-gifted education institute in metropolitan area were selected to participate in the field trip program. After the program, the students answered a survey regarding their perceptions of the program. Additionally, 19 students were selected for group interviews to further explore their survey responses. Results showed that many elementary science-gifted students perceived the program positively in various cognitive and affective aspects. Some students also pointed out certain limitations of the program. Five interconnected learning experiences were identified as influencing the students' perceptions: "experiences fostering creativity", "non-residential camp-type project-based learning experiences", "self-directed learning experiences", "experiences utilizing digital devices", and "collaborative experiences". Educational implications regarding these results were discussed.

• Journal of Gifted/Talented Education
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• v.21 no.4
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• pp.961-977
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• 2011

The purpose of this study was to investigate the relationship between learning motivation and task commitment and find sub factors of learning motivation that affect task commitment. For this study 30 science gifted student (4th and 5th grade in elementary school) participated. The survey instruments used for this study were Academic Motivation Scale and Task Commitment Scale. The statistical methods employed for data analysis were the correlation analysis and multiple regression analysis. The result of this study were as follows: First, the learning motivation and task commitment of science gifted students showed similar levels. But there was differences of strength each sub factors of learning motivation and task commitment. Second, there was a significant positive correlation between learning motivation and task commitment. Also, learning motivation has the explanatory power of predictive variable for the task commitment approximately 49.3%. Expecially learning motivation has significant positive correlation with responsibility and self-control that sub factors of task commitment. Among the sub factor of learning motivation, confidence has most correlations with sub factors of task commitment and significant impact on task commitment. This result indicate that we need to develop learning motivation to improve task commitment and especially develop learning motivation program to grow up confidence of science-gifted.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.619-623
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• 2018

This paper aimed to design a computer course teaching-learning strategy for (m-learning?) to be used in a Problem Based Learning (PBL) environment. The research findings were as follows. Firstly, learning contents were provided as educational tools for mobile device usage. The educational contents provided were designed for effective usage on mobile devices, such as smartphones, thereby making mobile devices suitable for use as learning tools. Secondly, learning contents for PBL were provided. PBL problems (for computer engineering courses) were made with the principles of teaching plans. The learning objectives were achieved through the problem-solving progress of the learners and their self-directed and cooperative learnings. Thirdly, learning resources were provided that were easily accessible through smartphones, laptops and PDAs. This study is about the PBL instructional design of creative engineering design subjects, which aims to foster talent. The PBL model developed in this study consists of Analysis, Design, Development, Implementation, and Evaluation. We made a plan for creative engineering design subjects based on PBL, and focused on the process of PBL. This research was able to establish the basis for PBL usage in Engineering Schools and help achieve its ultimate goal of endowing professional intellectuals with creative problem-solving abilities.

• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.385-396
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• 2019

This study was conducted to explore the characteristics of learner who showed free riding behavior in online collaborative learning. For this, 290 students from three universities in the metropolitan area were studied. The collected data are as follows. Learner characteristics are learning strategy, learning motivation, academic retardation behavior, and learning disposition. Interaction distinguished between frequency and type of message. Interaction levels were collected with frequency. The subjects with less than 5 interaction frequencies were defined as free-riding students. 43 students were classified as free riders. Learner characteristics were analyzed by cluster analysis. As a result, the learner characteristics were divided into five groups. All the free riding students belonged to 4 groups. The learner characteristics of 4 groups are as follows. First, the level of the learning strategy is very low. Second, learning motivation has a high tendency toward performance - oriented approach and high tendency to avoid performance. This tends to deliberately avoid learning. Third, the level of delayed behavior is high. This is deliberately putting off student activities. Fourth, learning tendency is high in academic anxiety, task value, self efficacy and learning belief are very low. This is a lack of confidence in learning.