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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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STUDENT PERSPECTIVE ON THE USE OF AI IN HIGHER EDUCATION: ANALYSIS OF THE STUDENTS’ PODCAST 32

STUDENT PERSPECTIVE ON THE USE OF AI IN HIGHER EDUCATION: AN...

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32nd Interdisciplinary Information Management Talks: Changes to ICT, Management, and Business Processes through AI, IDIMT 2024

作者： Kilz, Lydia Dresden University of Technology Center for interdisciplinary Learning and Teaching Germany

ISBN: (纸本)9783991515272

This paper examines students' perceptions of AI in higher education, with insights drawn from a Virtual Collaborative learning (VCL) course. Qualitative analysis of student-generated podcasts revealed a range of applications for AI tools, including text enhancement and learning support. While students recognise the potential of AI to enhance teaching efficiency and personalisation, they emphasise the irreplaceable role of human interaction in education. The preferences for AI integration are centred on transparency, ethical use, and augmentation rather than the dominance of the learning process. However, concerns about uncritical AI reliance and potential erosion of critical thinking skills have been identified. These findings underscore the need for a balanced approach to AI integration in education, guided by ethical considerations and pedagogical principles. Educators must foster digital literacy and critical thinking skills while addressing concerns about autonomy and intellectual agency. Future research may examine the long-term impact of AI on learning outcomes, the implications for equity, and strategies for responsible AI use. © IDIMT *** rights reserved.

关键词： Collaborative learning

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A Framework Based on the DAO and NFT in Blockchain for Electronic Document Sharing

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2373-2395页

作者： Lin Chen Jiaming Zhu Yuting Xu Huanqin Zheng Shen Su Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Engineering Research Center of lntegration and Application of Digital Learning Technology Ministry of EducationBeijing100816China

In the information age,electronic documents(e-documents)have become a popular alternative to paper documents due to their lower costs,higher dissemination rates,and ease of knowledge ***,digital copyright infringements occur frequently due to the ease of copying,which not only infringes on the rights of creators but also weakens their creative ***,it is crucial to establish an e-document sharing system that enforces copyright ***,the existing centralized system has outstanding vulnerabilities,and the plagiarism detection algorithm used cannot fully detect the context,semantics,style,and other factors of the *** watermark technology is only used as a means of infringement *** paper proposes a decentralized framework for e-document sharing based on decentralized autonomous organization(DAO)and non-fungible token(NFT)in *** use of blockchain as a distributed credit base resolves the vulnerabilities inherent in traditional centralized *** e-document evaluation and plagiarism detection mechanisms based on the DAO model effectively address challenges in comprehensive text information checks,thereby promoting the enhancement of e-document *** mechanism for protecting and circulating e-document copyrights using NFT technology ensures effective safeguarding of users’e-document copyrights and facilitates e-document ***,recognizing the security issues within the DAO governance mechanism,we introduce an innovative optimization *** experimentation,we validate the enhanced security of the optimized governance mechanism,reducing manipulation risks by up to 51%.Additionally,by utilizing evolutionary game analysis to deduce the equilibrium strategies of the framework,we discovered that adjusting the reward and penalty parameters of the incentive mechanism motivates creators to generate superior quality and unique e-documents,while evaluators are more likely to engage in

关键词： Electronic document sharing blockchain DAO NFT evolutionary game

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Assessing the impact of differential privacy in transfer learning with deep neural networks and transformer language models

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Neural Computing and Applications 2025年第6期37卷 5097-5119页

作者： Sousa, Samuel Trügler, Andreas Kern, Roman Know Center Research GmbH Graz Austria Graz University of Technology Graz Austria Graz Center for Machine Learning Graz Austria University of Graz Graz Austria

The realization of trustworthy artificial intelligence strongly relies on privacy, fairness, and accountability requirements. Although model trustworthiness results from the synergy between these requirements, some effects often clash with one another and may propagate issues into downstream tasks. This paper empirically indicates that optimization algorithms with differential privacy (DP), such as differentially private stochastic gradient descent (DP-SGD) and differentially private Adam (DP-Adam), disproportionately increase differences in accuracy between groups and domains in transfer learning based on convolutional neural network (CNN) and long-short-term memory (LSTM) models. We investigate the impact of DP-SGD and DP-Adam in transfer learning models, which receive DP noise to hinder privacy risks and perform predictions on target domain data. In addition, after extensive experimental evaluation, we provide evidence that fine-tuning may reduce the differences in accuracy for most groups compared to gradient boosting and ensemble methods. However, differentially private optimizers may reverse gains in accuracy from transfer learning for CNNs and LSTMs. Unevenly reduced model accuracy may jeopardize the predictions’ fairness, causing numerous real-world implications, such as denying credit to people in need or undermining complex language processing. Identifying scenarios where privacy protection conflicts with fairness for transfer learning is a problem often overlooked yet directly aligned with the accountability requirement in trustworthy artificial intelligence. Therefore, assessing the impact of differentially private optimizers in transfer learning is the first step to developing mitigation strategies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Differential privacy

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A Machine learning-Based Framework for Predicting E-Commerce Product Ratings 21

A Machine Learning-Based Framework for Predicting E-Commerce...

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21st International Bhurban Conference on Applied Sciences and technology, IBCAST 2024

作者： Iqbal, Muneeb Hamayat, Faizan Ahmad, Rana Fayyaz Husnain, Syed Khursheed Artificial Intelligence Technology Center National Center for Physics Quality Management Department Islamabad Pakistan Machine Learning and Deep Learning Lab Artificial Intelligence Technology Center National Center for Physics Islamabad Pakistan

ISBN: (纸本)9798331516680

Big data has been the center of attention for industry due to the volume of data that is being generated and growing over time. Machine learning algorithms are being employed more and more to anticipate various features of products. Product ratings are one of the key metrics used by every firm to evaluate the performance of its products. The product rating methodology persuades customers to purchase any item. It also demonstrates a product's potential for success in the consumer market. In order to forecast a product's rating based on its category, listed price, reduced price, and number of ratings, this study used a machine learning model. The Amazon dataset, which is accessible on Kaggle, was used in this study. This study evaluates various machine learning models using a dataset of 61,950 electronics appliance entries from Amazon to predict product ratings based on category, listed price, reduced price, and number of ratings. The Random Forest model outperforms others, with a precision of 0.65 and recall of 0.6 for 1-star ratings, and demonstrates superior performance for 2-star, 4-star, and 5-star predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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Computational Thinking Based STEM Art Exhibits 7th

Computational Thinking Based STEM Art Exhibits

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7th International Conference on Creative Mathematical Sciences Communication, CMSC 2024

作者： Thakkar, Jay Jain, Manish Center for Creative Learning Indian Institute of Technology Gandhinagar Gujarat382355 India

ISBN: (纸本)9783031732560

Making large-scale STEM exhibits can be a very engaging group activity for students across all ages. Apart from giving them a sense of accomplishment from completing the mammoth task of exhibit making, it also inspires them to think about the underlying algorithm that generated the design. In this paper, we describe exhibit designs based on pixel art using materials such as dice, bindis, Rubik’s cubes, strings, tessellation tiles, sticky notes, push-pins etc. We also share our experience and learnings from making 25+ different large scale portraits with students from elementary school to undergraduates. Affordable raw-materials and open-source tools make the designs accessible for use by educators in their schools. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Pixels

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A Review on the Security of the Ethereum-Based DeFi Ecosystem

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 69-101页

作者： Yue Xue Dunqiu Fan Shen Su Jialu Fu Ning Hu Wenmao Liu Zhihong Tian Cyberspace Institute of Advanced Technology Guangzhou UniversityGuanzhou510000China Innovation Center NSFOCUS Inc.Beijing100089China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of EducationBeijing100816China

Decentralized finance(DeFi)is a general term for a series of financial products and *** is based on blockchain technology and has attracted people’s attention because of its open,transparent,and intermediary *** them,the DeFi ecosystem based on Ethereum-based blockchains attracts the most ***,the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few ***,we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security *** that end,we investigate the Ethereum-based DeFi security issues:1)inherited from the real-world financial system,which can be solved by macro-control;2)induced by the problems of blockchain architecture,which require a better blockchain platform;3)caused by DeFi invented applications,which should be focused on during the project *** on that,we further discuss the current solutions and potential directions ofDeFi *** to our research,we could provide a comprehensive vision to the research community for the improvement of Ethereum-basedDeFi ecosystem security.

关键词： Blockchain smart contract decentralized finance DeFi security

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Practical Research of Deep learning Algorithm in Improving the Efficiency of Information Interaction between Network Security and Privacy Protection

Practical Research of Deep Learning Algorithm in Improving t...

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2024 International Conference on Interactive Intelligent Systems and Techniques, IIST 2024

作者： Chen, Weijie Du, Zhongkang Xiao, Xinguang Chen, Guodong Zhang, Han Zhang, Xuxia State Grid of China Technology College E-Learning Service Center Jinan China

ISBN: (纸本)9798350374421

With the rapid advancement of information technology, there is a growing concern for network security and personal privacy protection. This study aims to explore the practical application of deep learning algorithms in enhancing the efficiency of information interaction between network security and privacy protection. Utilizing deep learning as a tool, a joint model based on deep learning is developed to delve into the realms of network security and privacy protection comprehensively. Firstly, the paper examines the current use of deep learning algorithms in network security and proposes enhancement strategies for existing challenges. By refining and enhancing existing deep learning models, a joint deep learning model is introduced, effectively detecting abnormal behaviors and threats in the network, thereby elevating network security. Secondly, the study investigates the role of deep learning algorithms in streamlining the efficiency of privacy protection information exchange. Through the analysis of user behavior patterns and data traffic, a deep learning model for assessing user privacy risks is formulated, enabling timely identification of potential privacy breaches and the implementation of preventive measures. Lastly, a comprehensive evaluation of the joint deep learning model's performance on training, validation, and test datasets is conducted, comparing it with traditional methods. Experimental results demonstrate the significant improvements achieved by the proposed deep learning model in enhancing the efficiency of information exchange between network security and privacy protection. It provides valuable insights for further research and application in the domain of network security and privacy protection. © 2024 IEEE.

关键词： Differential privacy

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Exploring implicit influence for social recommendation based on GNN

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Soft Computing 2024年第19期28卷 11001-11014页

作者： Liu, Zhewei Yang, Peilin Hao, Qingbo Zheng, Wenguang Xiao, Yingyuan Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

In recent years, the method of using graph neural networks (GNN) to learn users’ social influence has been widely applied to social recommendation and has shown effectiveness, but several important challenges have not been well addressed: (i) Most work fails to consider the user interests (historical user-item interactions) when first building user-user social relationships, which can make it difficult to capture accurate user embedding and thus prevent the model from better exploring the users’ social influence;(ii) Most of the current methods do not build social neighbors (with the same item-user interaction) that belong to the item and do not aggregate information from the perspective of social neighbors, which makes it possible for the item to lose a lot of details when expressing the user’s interest factors. Therefore, to address the above challenges, we propose Exploring Implicit Influence for Social Recommendation Based on GNN (EIIGNN). First, we construct the initial user embedding with user-item interaction information and use the implicit modeling module in user modeling to explore the implicit influence of interest factors on users. In addition, EIIGNN models the social graph structure of item (an item-item graph) so that item can aggregate information from the perspective of their social neighbors, which helps the model learn a more accurate representation of the item. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of EIIGNN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graph neural networks

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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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