• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.143-157
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• 2022

The aim of this study is to design Spudsville, an immersive game environment in Minecraft that can effectively help learners acquire the English language. To create a successful learning experience using Minecraft, the researchers adopted the Agile Model and the Design Thinking approach. The researchers first conducted an analysis through an extensive literature review in order to assess the learners' needs. Afterwards, they designed and developed a Minecraft world based on the data collected during the analysis phase. The researchers learned that implementing constructivist and behaviorist approaches has benefits, even though applying a cognitivist-learning model to Spudsville could have provided the researchers with more insight on how learner processes information. Making these adjustments could improve Spudsville's effectiveness and could potentially help the ways in which gamified learning aids with language acquisition.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.181-192
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• 2017

This study aims to examine the educational effects of the SSI program using Digital Storytelling (DST) approaches. Since DST provides students opportunities to express their own opinions in the form of stories and to share learning outcomes through the web, we developed and implemented SSI program by adopting the concept of DST in order to produce synergistic effects on student learning. Twenty-four 9th graders who enthusiastically engaged in the DST-based SSI program participated in this study. The students responded to focus group interviews after the instruction, and all interviews were transcribed for analysis. The results indicated that the students became aware of socio-ethical perspectives of each SSI topic while searching and collecting data by themselves. They also felt the necessity to consider multiple perspectives around the issues by having discussions with group members. Second, pre-producing DST allowed students to negotiate to settle on a group discussion, and to use emotional contents that can lead viewers to have sympathy. In addition, while producing DST, students considered various factors such as design, soundtrack, visual effects, and screen composition in order to express their opinions and convey their messages more effectively. In the stage of sharing DST outcomes and receiving feedback, they realized new perspectives that they did not perceive in the previous production process, and to move them into an action for resolving the problems caused by SSI. This study showed the potentials of DST-based SSI instruction as a good strategy to support students' SSI engagement.

• Journal of Information Science Theory and Practice
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• v.3 no.4
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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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• v.15 no.3
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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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• v.5 no.1
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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.

• Hwankyungkyoyuk
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• v.17 no.1
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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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• v.22 no.3
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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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• v.17 no.12
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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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• v.12 no.6
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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.

• Research in Mathematical Education
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• v.26 no.1
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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.