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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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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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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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Benchmarking One Class Classification in Banking, Insurance, and Cyber Security 12th

Benchmarking One Class Classification in Banking, Insurance,...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Priyanka, Chaduvula Vivek, Yelleti Ravi, Vadlamani Center for Artificial Intelligence and Machine Learning Institute for Development and Research in Banking Technology Hyderabad500076 India

ISBN: (纸本)9789819601387

Given the inherent rarity observed in imbalanced datasets, adopting one-class classification (OCC) stands out as a pragmatic approach to counteract bias toward the predominant class. This research endeavors to thoroughly assess ten state-of-the-art OCC methodologies across a spectrum of five diverse challenges within the Banking, Insurance, and Cybersecurity sectors. Moreover, we introduce an innovative unsupervised learning technique wherein (i) K-Means clustering is utilized during the training phase on negative sample data. To determine the optimal number of clusters, we utilized the Silhouette index and employed the maximum intra-class centroid distance as a cluster-specific threshold. These thresholds play a pivotal role in distinguishing between positive and negative samples. (ii) In the testing phase, a majority voting mechanism is employed to evaluate the discriminative capability of these thresholds, facilitating precise classification of test data. Empirical findings unequivocally demonstrate that our proposed approach outperforms several state-of-the-art techniques, achieving superior classification accuracy across four out of the five datasets. This underscores the effectiveness and potential applicability of our novel methodology in tackling the intricate challenges prevalent in the Banking, Insurance, and Cybersecurity sectors, particularly in the domains of fraud detection and related areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Banking

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DIGITAL TRANSFORMATION PROCESS IN TUTOR TRAINING: FROM FACE-TO-FACE TRAINING TO THE VIRTUAL ESCAPE ROOM FOR ENHANCING TEACHING COMPETENCIES 21

DIGITAL TRANSFORMATION PROCESS IN TUTOR TRAINING: FROM FACE-...

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21st International Conferences on Mobile learning, ML 2025 and 10th International Conference on Educational Technologies, ICEduTech 2025

作者： Pippig, Michelle Hänsel, Sabrina Dresden University of Technology Center for Interdisciplinary Learning and Teaching Mommsenstraße 6 Dresden01069 Germany

ISBN: (纸本)9789898704665

This paper presents the digital transformation process of a face-to-face training programme at the Technical University of Dresden into mobile learning solutions aimed at improving the teaching skills of student tutors in higher education. It explores the development of a 360-degree learning environment that enables student tutors to access training content flexibly and independently of location. Furthermore, it demonstrates how this learning environment is extended through the development of a virtual escape room. By incorporating elements of gamification and artificial intelligence, the virtual escape room is designed to provide an immersive and customisable learning environment that specifically fosters competencies such as critical thinking, problem solving and collaboration. This approach addresses challenges such as accessibility, motivation, and skills development, while supporting the broader goals of digital transformation in higher education. The paper highlights the potential of mobile technologies for the sustainable development of tutor training. © 2025 Proceedings of the International Conferences on Mobile learning 2025 and Educational Technologies 2025. All rights reserved.

关键词： Adversarial machine learning

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Convergence Analysis of Probability Flow ODE for Score-based Generative Models

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IEEE Transactions on Information Theory 2025年第6期71卷 4581-4601页

作者： Huang, Daniel Zhengyu Huang, Jiaoyang Lin, Zhengjiang Peking University Beijing International Center for Mathematical Research Center for Machine Learning Research Beijing China University of Pennsylvania PhiladelphiaPA United States Massachusetts Institute of Technology Department of Mathematics MA United States

Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions. Despite their effectiveness, their theoretical underpinnings remain relatively underdeveloped. In this work, we study the convergence properties of deterministic samplers based on probability flow ODEs from both theoretical and numerical perspectives. Assuming access to L2-accurate estimates of the score function, we prove the total variation between the target and the generated data distributions can be bounded above by O(d3/4δ1/2) in the continuous time level, where d denotes the data dimension and δ represents the L2-score matching error. For practical implementations using a p-th order Runge-Kutta integrator with step size h, we establish error bounds of O(d3/4δ1/2 + d · (dh)p) at the discrete level. Finally, we present numerical studies on problems up to 128 dimensions to verify our theory. © 1963-2012 IEEE.

关键词： Continuous time systems

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On the Role of Priors in Bayesian Causal learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1439-1445页

作者： Geiger, Bernhard C. Kern, Roman Graz University of Technology Signal Processing and Speech Communication Laboratory Graz8010 Austria Know Center Research GmbH Graz8010 Austria Graz University of Technology Institute of Machine Learning and Neural Computation Graz8010 Austria

In this work, we investigate causal learning of independent causal mechanisms (ICMs) from a Bayesian perspective. Confirming previous claims from the literature, we show in a didactically accessible manner that unlabeled data (i.e., cause realizations) do not improve the estimation of the parameters defining the mechanism. Furthermore, we observe the importance of choosing an appropriate prior for the cause and mechanism parameters, respectively. Specifically, we show that a factorized prior results in a factorized posterior, which resonates with Janzing and Schölkopf's definition of ICMs via the Kolmogorov complexity of the involved distributions and with the concept of parameter independence of Heckerman et al. © 2020 IEEE.

关键词： Contrastive learning

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LLM Sensitivity Challenges in Abusive Language Detection: Instruction-Tuned vs. Human Feedback 31

LLM Sensitivity Challenges in Abusive Language Detection: In...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Yaqi Hangya, Viktor Fraser, Alexander School of Computation Information and Technology Technical University of Munich Germany Fraunhofer IIS Erlangen Germany Munich Center for Machine Learning Germany

ISBN: (纸本)9798891761964

The capacity of large language models (LLMs) to understand and distinguish socially unacceptable texts enables them to play a promising role in abusive language detection. However, various factors can affect their sensitivity. In this work, we test whether LLMs have an unintended bias in abusive language detection, i.e., whether they predict more or less of a given abusive class than expected in zero-shot settings. Our results show that instruction-tuned LLMs tend to under-predict positive classes, since datasets used for tuning are dominated by the negative class. On the contrary, models fine-tuned with human feedback tend to be overly sensitive. In an exploratory approach to mitigate these issues, we show that label frequency in the prompt helps with the significant over-prediction. © 2025 Association for Computational Linguistics.

关键词： Contrastive learning

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Research trends in science education from 2018 to 2022: a systematic content analysis of publications in selected journals

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International Journal of Science Education 2025年第4期47卷 510-538页

作者： Lin, Tzung-Jin Lin, Tzu-Chiang Potvin, Patrice Tsai, Chin-Chung Program of Learning Sciences School of Learning Informatics National Taiwan Normal University Taipei Taiwan Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan Center for Liberal Arts National Kaohsiung University of Science and Technology Kaohsiung Taiwan Center for Teacher Education National Kaohsiung University of Science and Technology Kaohsiung Taiwan Département de didactique Université du Québec à Montréal Montréal Canada

This study uncovers research trends by analysing 1,142 papers published in Science Education, Journal of Research in Science Teaching, and International Journal of Science Education: Part A between 2018 and 2022, followed by a series of systematic reviews dating back to 1998. The main findings indicate that, during the period of 2018–2022, the three most studied research topics were associated with learner characteristics and classroom contexts (learning-Context), teacher thinking/cognition and pedagogical issues (Teaching), and preservice education/in-service professional development (Teacher Education). An emerging interest in investigating the influence of cultural, social, and gender factors on science education (Culture, Social, and Gender) was observed. The analysis of the top 10 most-cited papers unveiled a notable focus on pertinent theoretical discussions and empirical research within the context of STEM/STEAM education. Besides, issues regarding engagement in and out of school settings, learners’ epistemologies, or sensemaking and science as practice were also highly cited. It is worth noting that there has been a rapid surge and new trend in research concerning science identity, garnering substantial attention by researchers as a meaningful lens for exploring relevant issues associated with participation and pipeline/career paths in STEM-related fields. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Literature review Research trend Science education Systematic content analysis

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GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering 31

GraphOTTER: Evolving LLM-based Graph Reasoning for Complex T...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Li, Qianlong Huang, Chen Li, Shuai Xiang, Yuanxin Xiong, Deng Lei, Wenqiang Sichuan University China Stevens Institute of Technology United States Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education China

ISBN: (纸本)9798891761964

Complex Table Question Answering involves providing accurate answers to specific questions based on intricate tables that exhibit complex layouts and flexible header locations. Despite considerable progress having been made in the LLM era, the reasoning processes of existing methods are often implicit, feeding the entire table into prompts, making it difficult to effectively filter out irrelevant information in the table. To this end, we propose GraphOTTER that explicitly establishes the reasoning process to pinpoint the correct answers. In particular, GraphOTTER leverages a graph-based representation, transforming the complex table into an undirected graph. It then conducts step-by-step reasoning on the graph, with each step guided by a set of pre-defined intermediate reasoning actions. As such, it constructs a clear reasoning path and effectively identifies the answer to a given question. Comprehensive experiments on two benchmark datasets and two LLM backbones demonstrate the effectiveness of GraphOTTER. Further analysis indicates that its success may be attributed to the ability to efficiently filter out irrelevant information, thereby focusing the reasoning process on the most pertinent data. Our code and experimental datasets are available at https://***/JDing0521/GraphOTTER. © 2025 Association for Computational Linguistics.

关键词： Question answering

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