• Journal of the Korean earth science society
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• v.31 no.5
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• pp.500-520
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• 2010

The purposes of this case study were (1) to explore one experienced teacher's views on Earth Systems Education and (2) to describe and document the characteristics of the Earth Systems Education (ESE) curriculum provided by an exemplary middle school science teacher, Dr. J. All the essential pieces of evidence were collected from observations, interviews with the experienced teacher and his eighth grade students, informal conversations, document analysis, and field notes. The $NUD^*IST$ for MS Windows was used for an initial data reduction process and to narrow down the focus of an analysis. All transcriptions and written documents were reviewed carefully and repeatedly to find rich evidence through inductive and content analysis. The findings revealed that ESE provided a conceptual focus and theme for organizing his school curriculum. The curriculum offered opportunities for students to learn relevant local topics and to connect the classroom learning to the real world. The curriculum also played an important role in developing students' value and appreciation of Earth systems and concern for the local environment. His instructional strategies were very compatible with recommendations from a constructivist theory. His major teaching methodology and strategies were hands-on learning, authentic activities-based learning, cooperative learning, project-based learning (e.g., mini-projects), and science field trips. With respect to his views about benefits and difficulties associated with ESE, the most important benefit was that the curriculum provided authentic-based, hands-on activities and made connections between students and everyday life experiences. In addition, he believed that it was not difficult to teach using ESE. However, the lack of time devoted to field trips and a lack of suitable resource materials were obstacles to the implementation of the curriculum. Implications for science education and future research are suggested.

• Smart Media Journal
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• v.10 no.4
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• pp.80-90
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• 2021

Using optical music recognition technology based on deep learning, we propose to apply the results derived to VR games. To detect the music objects in the music sheet, the deep learning model used YOLO v5, and Hough transform was employed to detect undetected objects, modifying the size of the staff. It analyzes and uses BPM, maximum number of combos, and musical notes in VR games using output result files, and prevents the backlog of notes through Object Pooling technology for resource management. In this paper, VR games can be produced with music elements derived from optical music recognition technology to expand the utilization of optical music recognition along with providing VR contents.

• Educational Technology International
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• v.13 no.1
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• pp.27-48
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• 2012

In this paper we are comparing how the OER (open educational resources) are developed in Higher Education in Finland and South Korea. We also present a comparison model for further studies. Essential findings based on our comparison are that in both countries there are many best practices of use of the OER and open learning. Open educational resources have great potential and their use can ensure quality teaching and learning. The activity has not inspired the great mass of higher education teachers in Finland and Korea. Traditionally, a teacher's job is working alone, and so a new operational culture is required. Our comparison indicates that numerous questions, fears and problems and cultural differences are also related to the thematic. There is an evident need for a new kind of strategic leadership, a new kind of teaching and learning culture and a doing together and production ideology for the method to spread. Based on our study the following interlinked elements of OER seem to be pivotal: changes to pedagogies, technology and operational culture; educational policy intention; and attitude to culture. Lastly, comparison frame by OER practice model is developed.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.901-910
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• 2023

In universities, programming language-based thinking and software education for non-majors are being implemented to cultivate creative and convergent talent capable of leading the digital convergence era in line with the Fourth Industrial Revolution. However, learners face difficulties in acquiring the unfamiliar syntax and programming languages. The purpose of this study is to propose a software education model to alleviate the challenges faced by non-major students during the learning process. By introducing algorithm techniques and diagram techniques based on programming language thinking and using the EZMKER kit as an instructional model, this study aims to overcome the lack of learning about programming languages and syntax. Consequently, a structured software education model has been designed and implemented as a top-down system learning model.

• The Korean Journal of Physiology and Pharmacology
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• v.28 no.6
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• pp.527-537
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• 2024

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants. Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.339-346
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• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.161-171
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• 2009

In this paper, we propose an automatic method for setting data constraints of a data cleansing service, which is for managing the quality of data exchanged between composite services based on SOA(Service-Oriented Architecture) and enables to minimize human intervention during the process. Because it is impossible to deal with all kinds of real-world data, we focus on business data (i.e. costumer order, order processing) which are frequently used in services such as CRM(Customer Relationship Management) and ERP(Enterprise Resource Planning). We first generate an extended-element vector by extending semantics of data exchanged between composite services and then build a rule-based system for setting data constraints automatically using the decision tree learning algorithm. We applied this rule-based system into the data cleansing service and showed the automation rate over 41% by learning data from multiple registered services in the field of business.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.55-64
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• 2017

This study identified the important categories and items about evaluation criteria of e-learning quality assurance by comparing evaluation criteria between Korea and France case. For deriving the conclusion, this research analyzed the Korea quality assurance case which is consist of success or failure for evaluation of quality assurance, and built the generic quality model of e-learning evaluation criteria. A generic model about evaluation criteria, categories, and item of e-learning quality assurance, which should be reflected on French quality criteria, were developed based on statistical approach. This research suggests a evaluation criteria which can be applied to African and Asian countries, that are related to AUF, as well as Korea. The result of this study can be applied to all organizations around the world which prepare for e-learning quality assurance, and at the same time it will be a valuable resource for companies or institutions which want to be evaluated e-learning quality assurance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.229-235
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• 2013

This paper proposed the method to reduce and manage the amount of using power by using the Self-Learning of inference engine that evolves through learning increasingly smart ways for each spaces with in the Space-Based Energy Management System (SEMS, Space-based Energy Management System) that is defined as smallest unit space with constant size and similar characteristics by using the collectible Big Data from the various information networks and the informations of various sensors from the existing Energy Management System(EMS), mostly including such as the Energy Management Systems for the Factory (FEMS, Factory Energy Management System), the Energy Management Systems for Buildings (BEMS, Building Energy Management System), and Energy Management Systems for Residential (HEMS, Home Energy Management System), that is monitoring and controlling the power of systems through various sensors and administrators by measuring the temperature and illumination.

• Journal of Family Resource Management and Policy Review
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• v.8 no.1
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• pp.47-60
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• 2004

Recognizing a tremendous increase in the Internet users and popularity of E-learning through the Internet, this study attempted to analyze interactive financial education web sites for children. Using meta search engines and major search engines, interactive financial education web sites identified based on the three criteria and analyzed in terms of the appropriateness for specific age groups, the coverage of contents related to the basic knowledge for financial literacy, and the interactive activities. The results showed that financial education web sites for children were needed to be improved in terms of both quantity and quality. The study also provides a guideline how to search for an appropriate financial education web sites for children when parents want teach about money to their children.