• 융합정보논문지
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• 제12권4호
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• pp.143-157
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• 2022

이 연구의 목적은 마인크래프트의 몰입형 게임 환경인 스퍼드빌을 디자인하여 학습자가 영어를 습득할 수 있도록 효과적으로 돕는 것이다. 마인크래프트를 사용하여 성공적인 학습 경험을 만들기 위해, 본 연구에서는 애자일 모델과 디자인 사고 접근법을 채택했다. 우선 학습자들의 요구를 분석하기 위해 광범위한 문헌 검토를 수행하였으며 이후의 분석 단계에서 수집된 자료를 바탕으로 마인크래프트 월드를 설계하고 개발하였다. 연구자들은 인지주의 학습 모델을 스퍼드빌에 적용하면 학습자가 정보를 처리하는 방법에 대한 더 많은 통찰력을 제공할 수 있고 한편으로 구성주의 및 행동주의 접근 방식을 구현하는 것이 또한 이점이 있다는 것을 알게 되었다. 마인크래프트 게임을 통하여 영어학습의 효과를 향상시킬 수 있으며 게임기반학습이 언어학습에 도움이 될 수 있는 잠재력을 확인할 수 있었다.

• 한국과학교육학회지
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• 제37권1호
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• pp.181-192
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• 2017

본 연구에서는 디지털스토리텔링을 적용한 SSI 교육프로그램(이하 DST-SSI 수업)을 개발하고, 학생들이 수업의 각 단계에서 인식하는 교육적 효과를 탐색해보았다. 디지털 기술을 활용하여 이야기를 제작하고 전달하는 디지털스토리텔링은 자신의 의견을 표현하는 동시에 학습의 결과를 공유하고 실천할 수 있는 기회를 제공하므로, 다양한 입장에 대한 이해와 타인과의 의견 조율이 요구되는 SSI 교육에 접목되었을 때 보다 상승효과를 가져올 수 있을 것으로 예상하였다. DST-SSI 수업은 교육과정과 밀접하게 연계되는 네 가지 주제를 선정한 후 각각의 SSI 주제에 적합한 DST 활동을 삽입하는 방식으로 구성되었으며, 본 연구에는 수업에 참여한 중학교 3학년 학생 중 24명이 참여하였다. 4명씩 그룹을 지어 포커스 그룹 면담을 진행하였으며, 면담내용은 모두 전사하여 분석에 이용하였다. 연구 결과, 학생들은 각각의 SSI 주제와 관련된 내용을 검색하고 자료를 수집하는 탐색 및 토의 과정에서 그동안 미처 인식하지 못했던 사회 윤리적 측면을 인식하게 되었으며, 주제를 둘러싼 입장이 매우 다양할 수 있음을 확인하고 수용하게 되었다. DST를 사전 제작하는 과정에서는 모둠의 의견을 효과적으로 반영하는 이야기를 전개하고자 다양한 관점을 조율했으며, 실생활에서 살펴볼 수 있는 자연스러운 상황을 주제로 하는 등 시청자의 공감을 이끌어낼 수 있는 감성적인 콘텐츠를 활용하는 방법을 고민하기도 하였다. 또한 학생들은 실제 DST를 제작하면서 자신이 전달하고자 하는 메시지를 보다 효과적으로 전달하기 위해 사운드트랙이나 시각효과, 화면 구성 등의 요소를 고려한 다양한 방법을 모색하였다. 마지막으로, 웹을 통한 디지털 스토리의 공유와 피드백 단계에서는 제작과정에서는 미처 깨닫지 못했던 내용을 확인하는 동시에, 해당 문제와 관련하여 실생활에서 실천할 수 있는 것을 찾아 행동으로 옮겨보는 노력을 한 것으로 드러났다.

• Journal of Information Science Theory and Practice
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• 제3권4호
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• pp.6-18
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• 2015

There has been an explosion of new research and writing about all aspects of the information disciplines. Nevertheless, both academics and practitioners often find it difficult to engage in successful writing strategies. Indeed, writing is hard work, and doing it in a way that leads to publication is an even harder task. Since reading is essential to good writing, the challenges of learning to write are obvious. In this essay, I am drawing on many years of experience in writing and publishing, as well as considerable reading of writers’ memoirs, advice books on writing, literary studies, and other perspectives on the experience of writing in order to offer a set of approaches that can be pursued over a lifetime of scholarship and practice. Writing is a craft or art to be learned, and learning demands paying attention to the audience, having clear objectives, being an avid reader, and possessing the ability to accept and learn from criticism. While information professionals and scholars incessantly write for each other, there are large segments of the public and other disciplines who they ignore. Fortunately, the tools and resources for improving one’s writing are both broad and deep; discipline and realistic strategies are all that are required to improve one’s writing and, ultimately, to achieve success in publishing.

• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.32-42
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• 2023

There are serious problems worldwide, such as a pandemic due to an unprecedented infection caused by COVID-19. On previous approaches, they invented medical vaccines and preemptive testing tools for medical engineering. However, it is difficult to access poor medical systems and medical institutions due to disparities between countries and regions. In advanced nations, the damage was even greater due to high medical and examination costs because they did not go to the hospital. Therefore, from a software engineering-based perspective, we propose a learning model for determining coronavirus infection through symptom data-based software prediction models and tools. After a comparative analysis of various models (decision tree, Naive Bayes, KNN, multi-perceptron neural network), we decide to choose an appropriate decision tree model. Due to a lack of data, additional survey data and overseas symptom data are applied and built into the judgment model. To protect from thiswe also adapt human normalization approach with traditional Korean medicin approach. We expect to be possible to determine coronavirus, flu, allergy, and cold without medical examination and diagnosis tools through data collection and analysis by applying decision trees.

• Advances in Energy Research
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• 제5권1호
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• pp.1-12
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• 2017

The world today has reached a certain level where it is impossible to get the quality education at the tertiary level without the use of Information and Communication Technology (ICT). ICT has made life better, communication easier and faster, teaching and learning more practical through computers and other technology based learning tools. However, despite these benefits ICT has equally contributed immensely to environmental problems. Therefore there is the need to use ICT resources efficiently in universities for environmental sustainability so as to save both the university environment and the world at large from the effects of global warming. This paper evaluates the carbon footprints from the use of ICT devices and comes up with a proposed green ICT framework to reduce the carbon footprints in universities. The framework contains techniques and approaches to achieve greenness in the data center, personal computers (PCs) and monitors, and printing in order to make ICT more environmentally friendly, cheaper, safer and ultimately more efficient. Concerned experts in their respective departments at Asia Pacific University of Technology and Innovation (APU) Malaysia evaluated the proposed framework. It was found to be effective for achieving efficiency, reducing energy consumption and carbon emissions.

• 한국환경교육학회지:환경교육
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• 제17권1호
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• pp.12-24
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• 2004

The purpose of this study was to analyze and compare Korean middle school-level environmental education(EE) textbooks. More specifically, these analyses and comparisons were designed to explore the extent to which environmental investigation skills were addressed in these EE textbooks (i.e., curriculum inclusion), as well as the manner in which these skills were to be taught and learned (i.e., instructional approaches). To analyze EE textbooks, the researchers developed a 'Curriculum Analysis Chart' that include six skill clusters and four instructional strategies. This analytic chart permitted the researchers to determine which skills were featured in selected textbooks, as well as which skill-oriented instructional strategies accompanied each of those skills. The chart was revised several times through pilot analyses. Using the final version of this chart, the researchers analyzed and then compared the three textbooks. This analysis indicated that the Korean middle school-level EE textbooks were designed to support conceptual learning and understanding of environment and environmental problems/issues (i.e., content-oriented), and were designed to support skill learning to a moderate degree. On the basis of textbooks analysis, the researchers offered several recommendations for future research, and for educational practices in EE.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.61-66
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• 2022

Object classification is one of the main fields in neural networks and has attracted the interest of many researchers. Although there have been vast advancements in this area, still there are many challenges that are faced even in the current era due to its inefficiency in handling large data, linguistic and dimensional complexities. Powerful hardware and software approaches in Neural Networks such as Deep Neural Networks present efficient mechanisms and contribute a lot to the field of object recognition as well as to handle time series classification. Due to the high rate of accuracy in terms of prediction rate, a neural network is often preferred in applications that require identification, segmentation, and detection based on features. Neural networks self-learning ability has revolutionized computing power and has its application in numerous fields such as powering unmanned self-driving vehicles, speech recognition, etc. In this paper, the experiment is conducted to implement a neural approach to identify numbers in different formats without human intervention. Measures are taken to improve the efficiency of the machines to classify and identify numbers. Experimental results show the importance of having training sets to achieve better recognition accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2177-2186
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• 2017

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제26권1호
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• pp.1-17
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• 2023

Over the last three decades, there has been an increasingly strong emphasis on group-centered approaches to mathematics teaching. One primary responsibility for teachers who use group-centered instruction is to "check in", or intervene, with groups to monitor group learning and provide mathematical support when necessary. While prior research has contributed valuable insight for successful teacher interventions in mathematics group work, there is a need for more fine-grained analyses of interactions between teachers and students. In this study, we co-conducted research with an exemplary middle grade teacher (Ms. Green) to learn about fine-grained details of her intervention practices, hoping to generate knowledge about successful teacher interventions that can be expanded, replicated, and/or contradicted in other contexts. Analyzing Ms. Green's practices as an exemplary case, we found that she used exceptionally short interventions (35 seconds on average), provided space for student dialogue, and applied four distinct strategies to support groups to make mathematical progress: (1) observing/listening before speaking; (2) using a combination of social and analytic scaffolds; (3) redirecting students to task instructions; (4) abruptly walking away. These findings imply that successful interventions may be characterized by brevity, shared dialogue between the teacher and students, and distinct (and sometimes unnatural) teaching moves.