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Journal of Research in Education Sciences 2023年第2期68卷 103-135页

作者： Hsu, Chen-Te Chen, Wu-Hsiung Weng, Tzu-Chun Kuo, Mei-Hui Lin, Yu-Wei Department of Recreation & Sport Management Shu-Te University Taiwan Department of Human Resource Development National Kaohsiung University of Science and Technology Taiwan Center for Physical Education Wenzao Ursuline University of Languages Taiwan Department of Physical Education and Sport Sciences National Taiwan Normal University Taiwan

Research Motivation and Objective Article 158 of the Constitution and the J.Y. Interpretation No. 450 (1998) of the University Act interpreted various freedom of teaching and university self-government principles. The Constitution and University Act allow universities to independently decide whether to offer physical education (PE) courses. PE courses must be offered in accordance with relevant laws. A decline in birth rates, university enrollment, university autonomy, and zero-credit courses have led to a reduction in compulsory PE courses. Martins et al. (2020) investigated PE in 54 countries and found that older students are less likely to engage in regular physical activity than younger students, highlighting the importance of maintaining exercise through the implementation of school-based PE courses. The present study examined the current situation and trends in the numbers of PE teachers in colleges in Taiwan following the introduction of Article No. 450. Additionally, laws, regulations, and systems were analyzed and relevant data was compiled to gain insights into trends in college-based PE courses. Literature Review The United States was the first country to include PE courses as a graduation requirement in higher education. In 1861, Amherst College began to require students to take PE courses during each year of enrollment, and several universities began to offer PE courses (Cardinal et al., 2012). By the late 1920s and early 1930s, approximately 97% of colleges and universities had PE as a requirement for graduation, and by the 1960s, 84% to 87% of schools included PE as a required course (Lumpkin & Jenkins, 1993). However, over the past three decades, in U.S. higher education, the percentage of schools that require completion of PE as a graduation requirement has declined (Hensley, 2000), and the percentage was as low as 39.55% in 2010 (Cardinal et al., 2012). Since 1991, the Japanese government has granted universities the flexibility to determine whethe

关键词： List of Physical Education Teachers in Colleges and Universities low-birth-rate secondary analysis

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Predicting Heart Disease Using Machine Learning Techniques on Electronic Health Records Data

Predicting Heart Disease Using Machine Learning Techniques o...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Waheeb Baddah Hamzah Ali Qasem Ayman Alsabry Abdulrahman Mohammed Hussein obaid Awadh abdo Mohammed F. E. Hanash Department of Information Technology Azal University for Human Development Sana'a Yemen International University of Technology Twintech 21 September university of medical and applied sciences Sana'a Yemen Department of Computer Science International University of Technology Twintech Sana'a Yemen medical information technology 21 September university of medical and applied sciences Sana'a Yemen biomedical equipments technology 21 September university of medical and applied sciences Sana'a Yemen International University of Technology Twintech Sana'a Yemen

heart disease is a prevalent cause of mortality worldwide, and the ability to identify and prevent this ailment at an early stage is crucial for enhancing patient outcomes. Recently, researchers have focused on utilizing Machine-Learning (ML) techniques to predict risk factors and the occurrence of heart disease. This study aims to identify the optimal parameters for accurately predicting the occurrence of heart disease through the fine-tuning of ML models' parameters. This study incorporated a blend of datasets from Cleveland, Hungary, Switzerland, and Long Beach V, which consisted of patients' medical histories and laboratory test outcomes. The collected data were employed to predict the occurrence of heart disease through diverse ML algorithms, such as KNN, decision tree, and SVM. Bayesian optimization was also utilized to fine-tune the hyperparameters of the ML algorithms for heart disease prediction. The findings demonstrated that the ML models achieved high accuracy in predicting the occurrence of heart disease, with the optimized KNN model achieving the highest accuracy rate of 100%.

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A Study of Image Recognition for Standard Convolution and Depthwise Separable Convolution 12th

A Study of Image Recognition for Standard Convolution and De...

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12th EAI International Conference on Bio-inspired Information and Communications Technologies, BICT 2020

作者： Tseng, Fan-Hsun Kao, Fan-Yi Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei10610 Taiwan

ISBN: (纸本)9783030571146

Artificial intelligence and deep learning techniques are all around our life. Image recognition and natural language processing are the two major topics. Through using TensorFlow-GPU as backend in convolutional neural network (CNN) and deep learning network, image recognition has been an extreme breakthrough in recent years. However, more and more model parameters result in overfitting problem and computation overhead. In the paper, the performance of image recognition between standard CNN and depthwise separable CNN is experimented and investigated. In addition, data augmentation technique is applied to both standard and depthwise separable CNNs to improve the image recognition accuracy. The experiments are implemented by an open source API called Keras with using CIFAR-10 dataset. Experimental results showed that the depthwise separable CNN improves validation accuracy compared with the standard CNN. Moreover, schemes with data augmentation achieve higher validation accuracy but training accuracy. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Convolution

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Enhancing Model Explainability in Financial Trading Using Training Aid Samples: A CNN-Based Candlestick Pattern Recognition Approach

Enhancing Model Explainability in Financial Trading Using Tr...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Yun-Cheng Tsai Jun-Hao Chen Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan R.O.C. Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan R.O.C.

Candlestick pattern recognition is a widely adopted technique in financial trading, leveraging visual patterns to analyze price movements. Deep Convolutional Neural Networks (CNNs) have exhibited remarkable accuracy in this domain. However, the increasing demand for transparency and explainability in CNN-based models raises concerns regarding their applicability in trading decision-making. This paper addresses these concerns by presenting a framework that enhances the explainability of CNN-based candlestick pattern recognition models. Our approach introduces an innovative data augmentation method to generate training aid samples, facilitating the model’s learning process within human domains. By incorporating this framework, traders gain valuable insights into the decision-making process, comprehending the rationale behind the model’s predictions. Our proposed approach exposes the inherent “black box” nature of CNN-based models, improving their interpretability and empowering traders to make well-informed decisions based on transparent and understandable insights. This advancement holds significant potential for enhancing decision-making processes in financial trading and fostering trust among traders.

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Analysis of Hindi-English Poetry Translation through Machine Translation Systems 5

Analysis of Hindi-English Poetry Translation through Machine...

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5th International Conference on Information Systems and Computer Networks, ISCON 2021

作者： Bansal, Jayshri Bansal, Pratosh Chakrawarti, Rajesh Kumar Human Resource Development Center Devi Ahilya Vishwavidyalaya MP Indore India Institute of Engineering Technology Devi Ahilya Vishwavidyalaya Department of Information Technology MP Indore India Institute of Engineering Technology Devi Ahilya Vishwavidyalaya Department of Computer Engineering Faculty of Engineering MP Indore India

ISBN: (纸本)9781665403412

Poetry is an expression of feelings. A poem is a thing which could be either sing or sometimes at least hummable. Over the years, specific rules have been defined for the structures and categories of the poems which can help in making them lyrical and rhythmic, although, all languages might have their own rules for the poems. Poetry translations through machines (computer systems) are very crucial and considered to be impossible tasks. Most of the times, Poetry translation of English language to other native languages may become quite easy but vice-versa is the toughest. The reasons are varieties in structures, categories and own emotions of the native languages. Numerous efforts have been applied around the world towards the poetries machine translations from native languages into English. This research presents rigorous survey for such kinds of poetry machine translation systems and their quality and accuracy measurement techniques. It also gives an idea of Hindi (our national and main native) language poetry machine translations into English Language. Further, a deep analysis and sources (corpuses) have been discussed for existing Hindi to English Machine Translations systems. At last, the research also discusses the importance of the Hindi to English Machine Translations systems [1] [2]. © 2021 IEEE.

关键词： Computational linguistics

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Management of face masks driven by the COVID-19 pandemic, possible implications and proposed policies: A household perspective

World Development Sustainability

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World development Sustainability 2023年 3卷

作者： Douti, Nang Biyogue Amuah, Ebenezer Ebo Yahans Acheampong, Emmanuel Dankwa, Paul Kazapoe, Raymond Webrah Agyemang, Edna Pambour Department of Environmental Science C. K. Tedam University of Technology and Applied Sciences Navrongo Ghana Department of Environmental Science Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Geological Engineering University for Development Studies Tamale Ghana Department of Geography and Resource Development University of Ghana Accra Ghana

Poor management of infectious waste in households poses severe health and environmental threats. This study was conducted in the Kassena-Nankana East Municipality of Ghana to assess the management of waste face masks/PPEs used in the prevention of the spread of the COVID-19 pandemic at the household level. Information was elicited from 150 households and two (2) institutions in charge of waste management to elicit information on the generation, management practices, and challenges in handling waste face masks/PPEs in the area. The study estimates that about 107,322 face masks are used daily in the area. Also, the rate of awareness of COVID-19 was strongly related (p = 0.002) to the generated volume of PPEs wasted. The need for face masks/PPEs as protection against COVID-19 largely influenced the rate of PPE waste generation (p = 0.005). However, the study found that 56% of households faced challenges in handling waste PPEs. Among them, 47% burned PPEs separately, 16% burned them with regular waste, and 27% discarded them with household waste. Improved waste management practices are needed to address these issues. Although there was no significant statistical association between age, gender, or occupation and awareness of face masks, there were some observed differences in awareness levels. The odds of respondents aged 18–25 being aware of face masks were 1.273 times greater than those aged 35 and older, while the odds of respondents aged 26–34 being aware of face masks were 2.181 times greater than those aged 35 and older. However, these differences may not be statistically significant. It is recommended that enhancing waste management procedures and educating people on the proper disposal of waste PPEs will be effective in the management of waste PPEs. © 2023 The Author(s)

关键词： COVID-19 Ghana Households Management and challenges PPEs Waste face masks

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Engagement, Academic Emotions, and Artificial Intelligence Application Creativity in the Metaverse: A Study of Facial Expressions

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Journal of Computer Assisted Learning 2025年第3期41卷

作者： Chang, Yu-Shan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan

Background: Due to the rapid development of artificial intelligence (AI) and the widespread adoption of online learning post-COVID-19, the metaverse has become an important strategy for innovative teaching. Objectives: This study aimed to investigate the impact of the metaverse on learning engagement, learning emotions, and creative performance in AI applications by analysing academic emotions through facial expressions. Methods: The study was conducted as a non-equivalent group pretest–posttest quasi-experimental construct involving 97 students from a public high school. The experimental teaching theme focused on AI image recognition and speech recognition, with the experimental group receiving online metaverse instruction and the control group receiving instruction through Google Meet. Results and Conclusion: Key findings include the following: the metaverse significantly and positively influenced cognitive, emotional, and social dimensions of learning engagement, but no impact was observed on the behavioural dimension;the metaverse had a substantial impact on the creative performance of AI application design, particularly in the empathy, definition, ideation, and testing stages;and the metaverse significantly affected academic emotions, with increased expressions of anger and sadness observed, particularly during the empathy, definition, and ideation stages of design thinking. Thus, the results of this study provide a pedagogical foundation for the adoption of the metaverse and auxiliary tools in teaching. © 2025 John Wiley & Sons Ltd.

关键词： academic emotions AI creativity engagement facial expression metaverse steam

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Impacts of interactions between peer assessment and learning styles on students’ mobile learning achievements and motivations in vocational design certification courses

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Interactive Learning Environments 2023年第3期31卷 1351-1363页

作者： Ting-Chia Hsu Wen-Li Chen Gwo-Jen Hwang a Department of Technology Application and Human Resource Development National Taiwan Normal University Taiwan b Learning Sciences and Assessment Academic Group National Institute of Education Nanyang Technological University Singapore c Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taiwand Department of Medical Humanities and Education School of Medicine Kaohsiung Medical University Kaohsiung Taiwan

ABSTRACTABSTRACTIn this study, a peer assessment-based mobile learning approach was implemented and evaluated in a vocational design certification course. A quasi-experiment was conducted to explore the impact of the proposed approach on the learning achievements and motivations of the students with active and reflective styles. It was found that the students using the peer assessment-based mobile learning approach generally learned better than those using the conventional mobile learning approach. It was also found that there were interactive effects between learning approaches and learning styles on intrinsic motivation. The students with an active learning style outperformed those with a reflective style in intrinsic motivation when conducting the peer assessment-based mobile learning. Active learning style students also had higher extrinsic motivation than those with a reflective learning style when conducting mobile learning.

关键词： Mobile learning peer assessment intrinsic motivation extrinsic motivation learning effectiveness

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Optimizing Heart Disease Prediction Models through SMOTE: Addressing Data Imbalance

Optimizing Heart Disease Prediction Models through SMOTE: Ad...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Waheeb Baddah Hamzah Ali Qasem Ayman Alsabry Rana Saleh Al Gawani Wafa Mohammed Alzuraiqi F. E. Hanash Azal University for Human Development International University of Technology Twintech Sana’a Yemen International University of Technology Twintech 21 September University of Medical and Applied Sciences Sana’a Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen Lebanese International University Sana’a Yemen Emirates International University International University of Technology Twintech Sana’a Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The problem of data imbalance poses a significant challenge in the field of medical diagnostics, particularly in heart disease prediction using machine learning models. This study investigates the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address this imbalance and improve the predictive accuracy of heart disease models. Through a rigorous methodology involving data collection, preprocessing, and the evaluation of 21 machine learning models, the study compares the performance of models trained on both original and SMOTE-balanced datasets. The findings indicate that models trained on SMOTE-balanced datasets showed statistically significant improvements in precision, F1-score, and often recall, with varied impacts on accuracy depending on the dataset. Key performance metrics such as accuracy, precision, recall, and F1-score are analyzed using t-tests to assess the statistical significance of improvements offered by SMOTE. Notably, the enhancements were particularly evident in precision and F1-score across the Cleveland and Statlog heart disease datasets, demonstrating SMOTE's ability to enhance model performance effectively. This study aims to demonstrate the potential of SMOTE in enhancing model performance, thus contributing to more effective and reliable heart disease diagnostics.

关键词： Heart Measurement Accuracy Machine learning Predictive models Data collection Data models

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Analytics in Higher Education: Scoping the Landscape of Research in the Area 11

Analytics in Higher Education: Scoping the Landscape of Rese...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Kaul, Natashaa Deshpande, Amruta Mittal, Amit Jain, Vishal Symbiosis Center for Management and Human Resource Development Pune India India Symbiosis International University Pune India Indira School of Business Studies Pgdm Pune India Chitkara Business School Chitkara University Punjab India School of Engineering and Technology Sharda University Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9781665487320

Understanding the research landscape of any domain is of primary importance at all stages of a concept's evolution. Research in the domain of analytics has increased exponentially in the past decade, analytics is a tool being applied to increase effectiveness and efficiency within all domains. Similarly, there are numerous research studies that have applied analytics to the domain of higher education. Assessing research activity is important for planning future research and identifying the way forward to make meaningful contributions. The objective of this paper is to examine the current research in this domain and to identify emerging areas of research to make meaningful contributions. Bibliometric analysis was applied using Scopus. Documents on analytics and higher education keywords in business, accounting and management and published in journals were considered for the analysis. Vos Viewer was used for bibliometric analysis in the area, to identify emerging research themes and to understand the scope of applying analytics in higher education. The search query found 1635 documents when using the keywords of 'analytics' and 'higher education'. This was further narrowed down to research in Business Management and Accounting and using Journal articles and conference articles, finally yielding 103 articles. After a primary read to remove articles misplaced in the group the final number of articles include for the bibliometric analysis came down to 98. There has been an increase in publications since 2018 with interest increasing post pandemic in the area. The research categories are Student outcomes and perspective, academician outcomes and perspective, design of learning systems, etc. Based on this, it is recommended that research should focus on theoretical aspects of applying analytics to HEIs, ethical issues in data collection, etc.. © 2022 IEEE.

关键词： Learning systems

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