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Effects of Performance Goal Orientations on English Translation Techniques in Digital Game-Based learning

International Journal of Human-Computer Interaction 2025年第9期41卷 5575-5590页

作者： Hung, Hsiu-Ting Yang, Jie Chi Chung, Ching-Jung Department of English National Kaohsiung University of Science and Technology Kaohsiung Taiwan Graduate Institute of Network Learning Technology National Central University Taoyuan Taiwan Research Center for Science and Technology for Learning National Central University Taoyuan Taiwan

Despite a growing body of literature on performance goals and learning, limited studies have explored their influence on English translation education within the context of Digital Game-Based learning (DGBL). To address this gap, this study developed a DGBL system to assist students in learning English translation techniques, and compared the effects of students’ different performance goal orientations (performance-approach versus performance-avoidance goals) on their learning performance, in-game performance, self-efficacy, fear of failure, and learning perceptions. The participants were 56 university students, who were divided into a performance-approach group (PAPG) and a performance-avoidance group (PAVG). The study used quantitative analysis with data from English translation technique tests, log data from gaming sessions, and three questionnaires. It was found that the PAPG exhibited significant improvements in relatively difficult translation techniques (i.e., conversion), while the PAVG showed notable improvements in relatively simple translation techniques (i.e., polysemy). Furthermore, the PAPG outperformed the PAVG in in-game performance related to polysemy. This study enhances our understanding of the impact of performance goal orientations on both learning and in-game performance in English translation education, particularly within the context of DGBL. © 2024 Taylor & Francis Group, LLC.

关键词： Students

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Task Offloading and Resource Allocation for Edge-Enabled Mobile learning

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China Communications 2023年第4期20卷 326-339页

作者： Ziyan Yang Shaochun Zhong School of Information Science and Technology Northeast Normal UniversityChangchun 130117China Engineering Research Center of E-Learning Supporting Technology Ministry of EducationChangchun 130117China

Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication networks,mobile edge computing,artificial intelligence,and mobile ***,due to the constrained data processing capacity of mobile devices,efficient and effective interactive mobile learning is a ***,for mobile learning,we propose a"Cloud,Edge and End"fusion system *** task offloading and resource allocation for edge-enabled mobile learning to reduce the time and energy consumption of user ***,we present the proposed solutions that uses the minimum cost maximum flow(MCMF)algorithm to deal with the offloading problem and the deep Q network(DQN)algorithm to deal with the resource allocation problem ***,the performance evaluation shows that the proposed offloading and resource allocation scheme can improve system performance,save energy,and satisfy the needs of learning users.

关键词： mobile learning mobile edge computing(MEC) system construction offloading resource allocation

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Masked Generative Light Field Prompting for Pixel-Level Structure Segmentations

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research 2024年第4期2024卷 533-544页

作者： Mianzhao Wang Fan Shi Xu Cheng Shengyong Chen The Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China Key Laboratory of Computer Vision and System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China

Pixel-level structure segmentations have attracted considerable attention,playing a crucial role in autonomous driving within the metaverse and enhancing comprehension in light field-based machine ***,current light field modeling methods fail to integrate appearance and geometric structural information into a coherent semantic space,thereby limiting the capability of light field transmission for visual *** this paper,we propose a general light field modeling method for pixel-level structure segmentation,comprising a generative light field prompting encoder(LF-GPE)and a prompt-based masked light field pretraining(LF-PMP)*** LF-GPE,serving as a light field backbone,can extract both appearance and geometric structural cues *** aligns these features into a unified visual space,facilitating semantic ***,our LF-PMP,during the pretraining phase,integrates a mixed light field and a multi-view light field *** prioritizes considering the geometric structural properties of the light field,enabling the light field backbone to accumulate a wealth of prior *** evaluate our pretrained LF-GPE on two downstream tasks:light field salient object detection and semantic *** results demonstrate that LF-GPE can effectively learn high-quality light field features and achieve highly competitive performance in pixel-level segmentation tasks.

关键词： prompt backbone integrate

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Meta-Auto-Decoder:a Meta-learning-Based Reduced Order Model for Solving Parametric Partial Differential Equations

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Communications on Applied Mathematics and Computation 2024年第2期6卷 1096-1130页

作者： Zhanhong Ye Xiang Huang Hongsheng Liu Bin Dong Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230027AnhuiChina Central Software Institute Huawei Technologies Co.LtdHangzhou 310007ZhejiangChina Center for Machine Learning Research Peking UniversityBeijing 100871China

Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational domains,*** reduced order modeling techniques accelerate the solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the ofline *** methods often need a predefined mesh as well as a series of precomputed solution snapshots,and may struggle to balance between the efficiency and accuracy due to the limitation of the linear *** the nonlinear representation of neural networks(NNs),we propose the Meta-Auto-Decoder(MAD)to construct a nonlinear trial manifold,whose best possible performance is measured theoretically by the decoder *** on the meta-learning concept,the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training *** adaptation to new(possibly heterogeneous)PDE parameters is enabled by searching on this trial manifold,and optionally fine-tuning the trial manifold at the same *** numerical experiments show that the MAD method exhibits a faster convergence speed without losing the accuracy than other deep learning-based methods.

关键词： Parametric partial differential equations(PDEs) Meta-learning Reduced order modeling Neural networks(NNs) Auto-decoder

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Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation 39

Behavior Importance-Aware Graph Neural Architecture Search f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ge, Chendi Wang, Xin Zhang, Ziwei Qin, Yijian Chen, Hong Wu, Haiyang Zhang, Yang Yang, Yuekui Zhu, Wenwu Department of Computer Science and Technology Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Machine Learning Platform Department Tencent TEG China

ISBN: (纸本)157735897X

Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in recommendation systems. While recent CDR approaches using graph neural networks (GNNs) capture complex user-item interactions, they rely on manually designed architectures that are often suboptimal and labor-intensive. Additionally, extracting valuable behavioral information from source domains to improve target domain recommendations remains challenging. To address these challenges, we propose Behavior importance-aware Graph Neural Architecture Search (BiGNAS), a framework that jointly optimizes GNN architecture and data importance for CDR. BiGNAS introduces two key components: a Cross-Domain Customized Supernetwork and a Graph-Based Behavior Importance Perceptron. The supernetwork, as a one-shot, retrain-free module, automatically searches the optimal GNN architecture for each domain without the need for retraining. The perceptron uses auxiliary learning to dynamically assess the importance of source domain behaviors, thereby improving target domain recommendations. Extensive experiments on benchmark CDR datasets and a large-scale industry advertising dataset demonstrate that BiGNAS consistently outperforms state-of-the-art baselines. To the best of our knowledge, this is the first work to jointly optimize GNN architecture and behavior data importance for cross-domain recommendation. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Online learning environments for transferable skills development: a systematic literature review from 2013–2022

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Interactive learning Environments 2024年第10期32卷 6894-6914页

作者： Weng, Cathy Kassaw, Kifle Astatke, Melese Yang, Chichuan Graduate Institute of Digital Learning and Education Empower Vocational Education research Center National Taiwan University of Science and Technology Taipei China Department of applied Foreign languages National Taiwan University of Science and Technology Taipei China

This review examined the impact of online learning environments (OLE) on acquiring transferable skills (TS) through an analysis of 31 papers from the Web of science database. Significant attention is given to critical thinking and communication skills, with moderate consideration given to creativity, problem-solving, and cooperation skills. Lesser emphasis is placed on negotiation, decision-making, self-management, resilience, and participation, while respect for diversity and empathy skills remains unexplored. STEM dominated the research focus in many articles, primarily focusing on higher education institutions, with limited attention to primary and secondary schools. learning management systems (LMS) and online platforms are widely used for teaching TS. Quantitative research methods predominated, emphasizing the cognitive dimension of TS, and limited exploration of the social dimension. Therefore, OLE significantly impacts TS development, contributing to a more knowledgeable society, improved education, lifelong learning, personal empowerment, enhanced employee skills, and entrepreneurial outcomes fostering economic development. OLE also plays a pivotal role in nurturing civic engagement, peace, and social harmony. However, some articles underscore negative consequences, including challenges in communication skill development, prolonged adjustment periods, and increased challenge for learners. According to the review's findings, the authors provide recommendations to teachers, educators, curriculum designers, and researcher. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： literature review online learning environments soft skills Transferable skills twenty-first-century skills

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Direct monocular vision algorithm based on deep constraints of point and line features fusion

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Optics Letters 2025年第12期50卷 3804-3807页

作者： Bai, Keqiang Li, Xiuhong Zhu, Yalan Wang, Feiyan Southwest University of Science and Technology Mianyang621010 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education 100039 China Chinese Academy of Agriculture Mechanization Sciences Group Co. Ltd. Beijing100101 China

The development of technology and the rapid increase of computing power have enabled the wide application of simultaneous localization and mapping (SLAM) in smart devices. Nevertheless, visual odometry based on the direct method exhibits inaccurate pose estimation in structured environments, because it ignores diverse line segment information, constraints of associated points, and estimated position information. Objective: This study aimed to address the issue of inaccurate pose estimation in structured environments for direct method-based visual odometry by proposing a direct monocular vision algorithm based on deep constraints of point and line features (DMVA-PLF), with the goal of improving pose estimation accuracy. Methods: The algorithm integrated line features from the environment into visual odometry through colinear and deep constraints, combined with historical pose information, to more effectively utilize image features and optimize the pose estimation process. Achievements: Experimental results demonstrated that the DMVA-PLF significantly improved pose estimation accuracy by efficiently leveraging image features, outperforming traditional methods in structured environments. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Fusion reactions

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A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

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Computer Modeling in Engineering & sciences 2024年第4期139卷 783-800页

作者： Lejun Zhang Junjie Zhang Kentaroh Toyoda Yuan Liu Jing Qiu Zhihong Tian Ran Guo College of Information Engineering Yangzhou UniversityYangzhou225127China Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou510006China Research and Development Center for E-Learning Ministry of EducationBeijing100039China Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A∗STAR)Singapore138632Singapore School of Physics and Materials Science Guangzhou UniversityGuangzhou510006China

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment *** the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model *** engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small *** this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and *** innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping ***,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization *** final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as *** testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful *** addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

关键词： Bitcoin multi-service classification graph maximization consensus data security

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Micro-expression Recognition Based on Dual-Stream Spatiotemporal Transformer 27th

Micro-expression Recognition Based on Dual-Stream Spatiotem...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Yan Huang, Xiaohua Tang, Chuangao Oulu School Nanjing Institute of Technology Nanjing China Ministry of Education Southeast University Nanjing China Research Center for Learning Science Southeast University Nanjing China

ISBN: (纸本)9783031782008

Micro-expressions, imperceptible spontaneous facial movements reflecting underlying emotions, hold significant importance in emotion recognition. Due to their short duration and low intensity, micro-expression recognition (MER) remains challenging. The collection of micro-expressions poses difficulties due to their characteristics, leading to a scarcity of spontaneous micro-expression datasets. Furthermore, existing methods typically utilize only one type of input for MER, thus failing to fully exploit the limited micro-expression samples. To address these issues, we propose a new dual-stream spatiotemporal transformer network combining optical flow and magnified micro-expression, enabling to handle different types of information, thereby providing richer and more comprehensive representations. By simultaneously inputting both original micro-expression images and the corresponding optical flow change images into the dual-stream net-work, we obtain a diverse range of micro-expression information, consequently mitigating the impact of the scarcity of micro-expression datasets. Experimental evaluations conducted on three public datasets, namely SMIC, SAMM, and CASME II, demonstrate the superiority of our approach over other methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical flows

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Humor Styles and Marital Satisfaction: Cluster Analysis of the Relationship

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Psychological Reports 2024年第5期127卷 2405-2426页

作者： Tsai, Meng-Ning Cheng, Yung-Chieh Chen, Hsueh-Chih Department of Educational Psychology and Counseling National Taiwan Normal University Taipei Taiwan Institute for Research Excellence in Learning Sciences Chinese Language and Technology Center National Taiwan Normal University Taipei Taiwan MOST AI Biomedical Research Center Ministry of Science and Technology Taipei Taiwan

Humor use in communication is considered an important factor that affects relationship satisfaction according to past studies. However, there is insufficient evidence on how humor influences marital satisfaction at the couple level. This study aimed to classify couples into clusters with different humor types based on their humor styles and to investigate whether the humor styles of husbands and wives influence marital satisfaction. In total, 170 couples completed a humor style and marital satisfaction inventory by analysis of covariance (ANCOVA) with grouping the data of their humor styles and using cluster analysis to determine whether there were differences in marital satisfaction among couples with different humor types. The results revealed five humor types for the couples: “positive humor couples,” “aggressive husband and self-defeating wife,” “humor denier husband and general humor wife,” “general humor husband and humor denier wife” and “humor denier couples.” The ANCOVA results indicate that the highest marital satisfaction occurs among couples with positive humor and the lowest is among couples with denier humor. This study illustrates the humor types that likely occur among couples and concludes that marital satisfaction is high when both partners use positive humor and low when neither partner uses humor. © The Author(s) 2023.

关键词： Cluster analysis humor styles humor types marital satisfaction married couples mental & physical health personality theory

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