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Status and trends of socioscientific issues in educational literature: insights and extensions from a co-word analysis

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International Journal of Science Education 2024年第11期46卷 1073-1097页

作者： Tang, Kai-Yu Lin, Tzu-Chiang Hsu, Ying-Shao Graduate Institute of Library & Information Science National Chung Hsing University Taichung Taiwan Innovation and Development Center of Sustainable Agriculture National Chung Hsing University Taichung Taiwan Center for Teacher Education National Kaohsiung University of Science and Technology Kaohsiung Taiwan Center for the Liberal Arts National Kaohsiung University of Science and Technology Kaohsiung Taiwan Graduate Institute of Science Education National Taiwan Normal University Taipei Taiwan Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan

The current study identified recent trends in socioscientific issues (SSIs) in the field of education. Through descriptive analysis and co-word analysis, selected articles published in journals listed in the Social Science Citation Index and Scopus from 2000 to 2021 were screened. A total of 334 papers were retrieved as research target samples from the Web of Science and Scopus databases after many rounds of searching and filtering. An analysis revealed an overall growing trend of SSI-related publications in the field of education. High-impact journals that published SSI-related research and highly cited articles were also identified. A series of co-word analyses were then conducted to determine co-occurring relationships among the keywords of the sample papers. This study extracted 39 author-defined keywords shared by at least three studies in the target samples. The analytic results indicated that ‘argumentation,’ ‘decision-making,’ ‘scientific literacy,’ and ‘knowledge’ were frequently used along with SSI as keywords. The co-word networks of the whole corpus of keywords and selected keywords were visualised to refine the common understanding of the educators and obtain the SSI research foci. This paper also offers future research directions. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： co-word analysis cognitive process learning outcome research trends Socioscientific issues systematic review

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Automated Prediction of Item Difficulty in Reading Comprehension Using Long Short-Term Memory

Automated Prediction of Item Difficulty in Reading Comprehen...

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International Conference on Asian Language Processing (IALP)

作者： Li-Huai Lin Tao-Hsing Chang Fu-Yuan Hsu Dept. of Computer Science and Information Engineering National Kaohsiung Univ. of Science and Technology Kaohsiung Taiwan Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan

ISBN: (数字)9781728150147

ISBN: (纸本)9781728150154

Standardized tests are an important tool in education. During the test preparation process, the difficulty of each test item needs to be defined, which previously relied on expert validation or pretest for the most part, requiring a considerable amount of labor and cost. These problems can be overcome by using machines to predict the difficulty of the test items. In this study, long short-term memory (LSTM) will be used to predict the test item difficulty in reading comprehension. Experimental results show that the proposed method has a good prediction for agreement rate.

关键词： Predictive models Semantics Training Logic gates Estimation Deep learning Computational modeling

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Empowering Optimal Control with Machine learning: A Perspective from Model Predictive Control

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IFAC-PapersOnLine 2022年第30期55卷 121-126页

作者： E Weinan Jiequn Han Jihao Long AI for Science Institute Beijing Center for Machine Learning Research and School of Mathematical Sciences Peking University Beijing China Center for Computational Mathematics Flatiron Institute New York 10010 USA Program of Applied and Computational Mathematics Princeton University Princeton 08544 USA

Solving complex optimal control problems have confronted computational challenges for a long time. Recent advances in machine learning have provided us with new opportunities to address these challenges. This paper takes model predictive control, a popular optimal control method, as the primary example to survey recent progress that leverages machine learning techniques to empower optimal control solvers. We also discuss some of the main challenges encountered when applying machine learning to develop more robust optimal control algorithms.

关键词： Machine learning Optimal Control Model Predictive Control

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Attribute prototype network for zero-shot learning

arXiv

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arXiv 2020年

作者： Xu, Wenjia Xian, Yongqin Wang, Jiuniu Schiele, Bernt Akata, Zeynep Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Cluster of Excellence Machine Learning University of Tübingen Germany University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

From the beginning of zero-shot learning research, visual attributes have been shown to play an important role. In order to better transfer attribute-based knowledge from known to unknown classes, we argue that an image representation with integrated attribute localization ability would be beneficial for zero-shot learning. To this end, we propose a novel zero-shot representation learning framework that jointly learns discriminative global and local features using only class-level attributes. While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features. We show that our locality augmented image representations achieve a new state-of-the-art on three zero-shot learning benchmarks. As an additional benefit, our model points to the visual evidence of the attributes in an image, e.g. for the CUB dataset, confirming the improved attribute localization ability of our image representation. Copyright © 2020, The Authors. All rights reserved.

关键词： Image representation

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ISLab at ROCLING 2023 MultiNER-Health Task: A Three-Stage NER Model Combining Textual Content and Label Semantics 35

ISLab at ROCLING 2023 MultiNER-Health Task: A Three-Stage NE...

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35th Conference on Computational Linguistics and Speech Processing, ROCLING 2023

作者： Wu, Jun-Jie Chang, Tao-Hsing Hsu, Fu-Yuan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Institute for Research Excellence in Learning Sciences Research Center for Psychological and Educational Testing National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9789869576963

ROCLING 2023 MultiNER-Health Task is to identify named entities in the healthcare domain. These recognized named entities need to be further classified into one of ten categories. This study proposes a threestage framework. The first stage of the framework is to recognize and categorize named entities using Label Semantics Model. This model employs both of textual content and label semantics to identify name entities and their categories. In the second stage, the Label Correction Model is used to correct abnormal labels. In the third stage, a rule-based approach is employed to revise the abnormal labels that cannot be corrected in the second stage. The proposed method is evaluated on three test sets provided by the task and its average F1score is 0.6838. It is the best performing model for SM test set among all participating task models. © 2023 ROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing. All rights reserved.

关键词： Semantics

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A systematic review of Stimulated Recall (SR) in educational research from 2012 to 2022

Humanities and Social Sciences Communications

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Humanities and Social sciences Communications 2024年第1期11卷 1-14页

作者： Zhai, Xuesong Chu, Xiaoyan Wang, Minjuan Tsai, Chin-Chung Liang, Jyh-Chong Spector, Jonathan Michael College of Education Zhejiang University Hangzhou China Hangzhou International Urbanology Research Center & amp Zhejiang Urban Governance Studies Center Hangzhou China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong Learning Design and Technology San Diego State University San Diego CA United States Program of Learning Sciences and Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan Department of Learning Technologies University of North Texas Denton TX United States

Stimulated Recall (SR) has long been used in educational settings as an approach of retrospection. However, with the fast growing of digital learning and advanced technologies in educational settings over the past decade, the extent to which stimulated recall has been effectively implemented by researchers remains minimal. This systematic review reveals that SR has been primarily employed to probe the patterns of participants’ thinking, to examine the effects of instructional strategies, and to promote metacognitive level. Notably, SR video stimuli have advanced, and the sources of stimuli have become more diverse, including the incorporation of physiological data. Additionally, researchers have applied various strategies, such as flexible intervals and questioning techniques, in SR interviews. Furthermore, this article discusses the relationships between different SR research items, including stimuli and learning contexts. The review and analysis also demonstrate that stimulated recall may be further enhanced by integrating multiple data sources, applying intelligent algorithms, and incorporating conversational agents enabled by generative artificial intelligence such as ChatGPT. This article provides a comprehensive analysis of SR studies in the realm of education and proposes a promising avenue for researchers to proactively apply stimulated recall in investigating educational issues in the digital era. © The Author(s) 2024.

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Atlas of Gray Matter Volume Differences Across Psychiatric Conditions: A Systematic Review With a Novel Meta-Analysis That Considers Co-Occurring Disorders

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Biological Psychiatry 2025年

作者： Fortea, Lydia Ortuño, Maria De Prisco, Michele Oliva, Vincenzo Albajes-Eizagirre, Anton Fortea, Adriana Madero, Santiago Solanes, Aleix Vilajosana, Enric Yao, Yuanwei Del Fabro, Lorenzo Solé, Eduard Verdolini, Norma Farré-Colomés, Alvar Serra-Blasco, Maria Picó-Pérez, Maria Lukito, Steve Wise, Toby Carlisi, Christina Arnone, Danilo Kempton, Matthew J. Hauson, Alexander Omar Wollman, Scott Soriano-Mas, Carles Rubia, Katya Norman, Luke Fusar-Poli, Paolo Mataix-Cols, David Valentí, Marc Via, Esther Cardoner, Narcis Solmi, Marco Zhang, Jintao Pan, Pinglei Shin, Jae Il Fullana, Miquel A. Vieta, Eduard Radua, Joaquim Institut d'Investigacions Biomèdiques August Pi I Sunyer Barcelona Spain Department of Medicine University of Barcelona Institute of Neuroscience Barcelona Spain Centro de Investigación Biomédica en Red de Salud Mental Instituto de Salud Carlos III Madrid Spain Bipolar and Depressive Disorders Unit Hospital Clínic Barcelona Spain Department of Biomedical and Neuromotor Sciences University of Bologna Bologna Italy Psychiatric and Psychology Service Hospital Clínic Barcelona Spain Schizophrenia Unit Hospital Clínic Barcelona Spain Department of Psychology The University of Hong Kong Hong Kong Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Pathophysiology and Transplantation University of Milan Milan Italy Local Health Unit Umbria 1 Department of Mental Health Mental Health Center of Perugia Perugia Italy Department of Addictive Behaviour and Addiction Medicine Central Institute of Mental Health Medical Faculty of Mannheim Heidelberg University Mannheim Germany eHealth ICOnnecta't Program and Psycho-Oncology Service Institut Català d'Oncologia L'Hospitalet de Llobregat Spain Psycho-oncology and Digital Health Group Health Services Research in Cancer Institut d'Investigació Biomèdica de Bellvitge L'Hospitalet del Llobregat Spain Live and Health Sciences Research Institute University of Minho Braga Portugal ICVS/3B's PT Government Associate Laboratory Guimarães Braga Portugal Departamento de Psicología Básica Universitat Jaume I Castelló de la Plana Spain Department of Child and Adolescent Psychiatry Institute of Psychology Psychiatry and Neuroscience King's College London London United Kingdom Department of Neuroimaging Institute of Psychiatry Psychology & Neurosciences King's College London London United Kingdom Division of Psychology and Language Sciences University College London London United Kingdom Centre for Affective Disorders Psychological Medicine King's College London Londo

Background: Regional gray matter volume (GMV) differences between individuals with mental disorders and comparison participants may be confounded by co-occurring disorders. To disentangle disorder-specific GMV correlates, we conducted a large-scale multidisorder meta-analysis using a novel approach that explicitly models co-occurring disorders. Methods: We systematically reviewed voxel-based morphometry studies indexed in PubMed and Scopus up to January 2023 that compared adults with major mental disorders (anorexia nervosa, schizophrenia spectrum, anxiety, bipolar, major depressive, obsessive-compulsive, and posttraumatic stress disorders plus attention-deficit/hyperactivity, autism spectrum, and borderline personality disorders) with comparison participants. Two authors independently extracted data and assessed quality using the Newcastle-Ottawa Scale. We derived GMV correlates for each disorder using: 1) a multidisorder meta-analysis that accounted for all co-occurring mental disorders simultaneously and 2) separate standard meta-analyses for each disorder in which co-occurring disorders were ignored. We assessed the alterations’ extent, intensity (effect size), and specificity (interdisorder correlations and transdiagnostic alterations) for both approaches. Results: We included 433 studies (499 datasets) involving 19,718 patients and 16,441 comparison participants (51% female, ages 20–67 years). We provide GMV correlate maps for each disorder using both approaches. The novel approach, which accounted for co-occurring disorders, produced GMV correlates that were more focal and disorder specific (less correlated across disorders and fewer transdiagnostic abnormalities). Conclusions: This work offers the most comprehensive atlas of GMV correlates across major mental disorders. Modeling co-occurring disorders yielded more specific correlates, supporting this approach's validity. The atlas NIfTI maps are available online. © 2024 Society of Biological Psychiatry

关键词： Anxiety disorders Comorbidity Gray matter volume Major depressive disorder Meta-analysis Psychiatric conditions

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The Development of the Mobile Interactive Virtual Nuclear Educator With AR and RAG for learning Nuclear Energy in Indonesia

The Development of the Mobile Interactive Virtual Nuclear Ed...

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International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Rio Nurtantyana Sahara Eka Kencana Murni Halim Hamadi Wu-Yuin Hwang Research Center for Data and Information Sciences & Center of Excellence Artificial Intelligence for Learning and Optimization National Research and Innovation Agency (BRIN) & Telkom University Bandung Indonesia Electronic Instrumentation Polytechnic Institute of Nuclear Technology Yogyakarta Indonesia Faculty of Science and Engineering & Graduate Institute of Network Learning Technology National Dong-Hwa University & National Central University Hualien Taiwan

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

The conventional way to search the information on the internet usually uses search engines, however, the results only provide textual information. There has been a noticeable gap in student engagement, particularly among young generations like elementary to high school students, who prefer interactive learning methods. Specifically, there is a need to focus on disseminating information about nuclear energy, particularly its application in nuclear power plants in accordance with Indonesia’s master plan about nuclear energy as it is essential for the younger generation to be informed. Therefore, we developed an Android mobile app namely AR-Deni with Augmented Reality (AR) technology that interactively provides information through touch and voice chat with an animated 3D virtual educator. To answer the question, it utilizes retrieval-augmented generation (RAG) technology with verified documents that could elevate students' curiosity and understanding about nuclear energy information while ensuring the accuracy and reliability of the information provided. Further, we used extreme programming (XP) as a development methodology to plan and test the app. We test the app with user acceptance testing (UAT) and performance testing with 12 scenarios to interact in Q&A. The results showed that the AR-Deni developed with the XP method and passed the 100% scenario in UAT. Regarding the performance, the RAG needed more response time to find the answer from specific questions rather than general questions. Hence, the AR-Deni could be used as an interactive virtual nuclear educator for learning media.

关键词： Nuclear energy Three-dimensional displays Search engines Media Mobile applications Time factors Reliability programming profession Power generation Testing

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The Development of A Digital Transformation Model for Higher Education Using the Object-Process Methodology

The Development of A Digital Transformation Model for Higher...

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International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Aria Bisri Rio Nurtantyana Abdurrakhman Prasetyadi Atina Putri Allya Paramita Koesoema Yusep Rosmansyah Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Center of Excellence Artificial Intelligence for Learning and Optimization Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

Digital transformation (DT) is an essential need for Higher Education Institution (HEI) in the era of modern technology to improve the quality of education and operational efficiency. This research develops a DT model using Object-Process Methodology (OPM), which integrates objects and processes into a single structured framework. This research utilized the results of a systematic literature review that had been carried out in previous studies to identify key components and challenges in DT. In detail, the OPM model was developed to map the interactions between technology infrastructure, business processes, human resources, and change management. Further, the model validation was conducted through three experts’ judgments. Therefore, the results show that our OPM model helps HEI to plan and implement DT strategies more effectively, improving the efficiency and quality of education. Hence, the author encourages HEI to use OPM model like in this study as a strategy to implement DT.

关键词： Digital control Digital transformation Computational modeling Education Process control Planning Informatics Periodic structures Guidelines Business

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Mathematical Model of Leptospirosis: Linearized Solutions and Stability Analysis

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Applied Mathematics 2013年第10期4卷 77-84页

作者： B. Pimpunchat G. C. Wake C. Modchang W. Triampo A. M Babylon Center of Excellence in Mathematics Bangkok Thailand Institute of Natural and Mathematical Sciences College of Sciences Massey University Auckland New Zealand Institute for Innovative Learning Mahidol University Nakorn Prathom Thailand Center of Excellence in Mathematics Bangkok Thailand R and D Group of Biological and Environment Physics (BIOPHYSICS) Department of Physics Faculty of Science Mahidol University Bangkok Thailand Industrial Mathematics Research Unit and Department of Mathematics Faculty of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand Center of Excellence in Mathematics Bangkok Thailand Institute of Natural and Mathematical Sciences College of Sciences Massey University Auckland New Zealand

In this paper the transmission of leptospirosis, an infectious disease caused by bacteria, is studied. Leptospirosis is currently spreading in Thailand and worldwide. A Susceptible-Infected-Removed sir model is used to study the stability analysis, analytical solution and global behavior of the spreading of the disease. The model was analysed using the techniques of non-linear dynamical systems. Two equilibrium points were found and the stability conditions for these equilibrium points were established. It will be shown that the linearised solutions of the sir equations are in good agreement with numerical solutions.

关键词： Technology Preference for Quality Volume of Trade Vertical Intra-Industry Trade

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