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A Transdisciplinary STEM Course Integrated through Project-based Learning on Robotics: Perspective from Teacher and Student Feedback

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Asia Pacific Journal of Education 2025年第2期45卷 661-676页

作者： Chang, Chi-Cheng Chen, Yu-Kai Department of Technology Application and Human Resource Development National Taiwan Normal University Taiwan

Project-based learning (PjBL) facilitates STEM learning, but the integrity of the project activities is a challenge and the design of integrated STEM courses through PjBL remains unclear. The purpose of this study was to design and evaluate a transdisciplinary STEM course integrated through P_jBL in robotics. Participants in the teaching experiment for four weeks were 54 high school students, including 29 males and 25 females. Based on semi-structured interviews with students and teachers and open-ended questionnaires from students, the course not only facilitated knowledge integration and application but also brought a positive effect on students’ learning outcomes. This study not only confirms that PjBL can be applied in the STEM course but also provides a practical example as a reference for designing transdisciplinary STEM courses integrated through PjBL. Finally, the implications for teaching practices and theories are also proposed. © 2023 National Institute of Education, Singapore.

关键词： integration Project-based learning robotics STEM, transdisciplinary

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Digital transformation anxiety: absorptive capacity, dynamic capability, and digital innovation performance

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Management Decision 2025年第3期63卷 734-755页

作者： Chang, Ching-Hsun Chen, Yu-Shan Tseng, Chin-Wei Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan Department of Business Administration National Taipei University New Taipei City Taiwan

Purpose: This study proposes the novel construct of digital transformation anxiety and investigates its effect, which is mediated by absorptive capacity and dynamic capability, on digital innovation performance. Design/methodology/approach: This study conducted a questionnaire survey among Taiwanese manufacturing and service companies to verify the research framework. A total of 130 valid responses were collected and analyzed using partial least squares structural equation modeling (PLS-SEM) and bootstrapping to test direct and mediation effects, respectively. Findings: Digital transformation anxiety negatively affects absorptive capacity and dynamic capability, whereas absorptive capacity and dynamic capability positively affect digital innovation performance. Dynamic capability more strongly mediates the association between digital transformation anxiety and digital innovation performance than absorptive capacity. Additionally, digital transformation anxiety does not negatively affect digital innovation performance. Finally, manufacturing companies had significantly higher levels of digital transformation anxiety than service companies. Research limitations/implications: This study proposes the novel construct of digital transformation anxiety to address a gap in the literature. Digital transformation anxiety leads companies to adopt unnecessarily conservative practices, preventing them from flexibly responding to technological advances. This insight highlights the negative effect of such anxiety on absorptive capacity and dynamic capability, extending the application of path dependency theory to companies. The findings underscore the value of enhancing dynamic capability and reallocating resources to foster digital innovation. The study identified and explored the concept of digital transformation anxiety and extended the perspective of dynamic capability to include digital transformation and digital innovation. Practical implications: The current findings indica

关键词： Absorptive capacity Digital innovation performance Digital transformation anxiety Dynamic capability

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Using mobile technology acceptance model to explore the satisfaction and continuous use of live streaming for training

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Interactive Learning Environments 2025年第2期33卷 1298-1321页

作者： Hsiao, Hsien-Sheng Lu, Shao-Yu Chang, Tze-Ling Lee, Chen-Shun Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan Institule for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan

During the digital era and especially the COVID-19 pandemic, live streaming emerged as a vital component of mobile learning and training. This study sought to determine whether the social presence and interactivity experienced by learners using live digital platforms could enhance their learning outcomes, satisfaction, and propensity for ongoing *** the mobile technology acceptance model as its research framework, this study integrated interactivity and social presence as external variables. The aim was to uncover how these variables impact user satisfaction and continued use, while also exploring the mediating role of learning effectiveness in driving continued use. A live streaming system designed for mobile training was tested among 67 employees in a pilot study. The results demonstrated that:(1)Ease of use positively impacted perceptions of mobile usefulness.(2)Mobile usefulness, interactivity, and social presence had a positive effect on satisfaction, yet ease of use didn't significantly influence satisfaction.(3)Interactivity enhanced social presence.(4)Satisfaction was a key driver of continued use, with learning effectiveness acting as a partial mediator between them. The findings from this research can guide businesses looking to utilize live streaming for training purposes and serve as a reference for academic research in similar areas. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： interactivity mobile learning mobile technology acceptance model social presence Transformation of learning

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Do attitudes matter? An investigation into students’ attitudes toward engineering and learning performances through engineering-focused STEM curricula

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International Journal of technology and Design Education 2025年第1期35卷 123-150页

作者： Tzeng, Sy-Yi Yu, Kuang-Chao Wu, Pai-Hsing Fan, Szu-Chun Department of Graphic Communications and Digital Publishing Shih-Hsin University Taipei City Taiwan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei City Taiwan Center for Textbook Research National Academy for Educational Research New Taipei City Taiwan Department of Industrial Technology Education National Kaohsiung Normal University No. 62 Shenzhong Rd. Yanchao Dist Kaohsiung City824 Taiwan

Many studies have investigated students’ attitudes toward engineering (ATE), but few have examined the changes in attitudes after attending an engineering design curriculum. This study analyzes these attitudinal changes in students attending an engineering-focused science, technology, engineering, and mathematics (EF-STEM) curriculum and investigates the influence and correlation between attitudes and learning performance. The participants included 364 tenth-grade students (ages 16–18 years). An 18-week experimental intervention design (2 h per week) was adopted. The Attitudes Towards Engineering Scale—Taiwan was applied before and after attending the EF-STEM course;remaining assessments were conducted post-course. The results showed that the ATE statistically decreased after attending the course, shifting from a positive attitude to a neutral-leaning attitude. Although most students appreciated the importance of engineering design, they exhibited a high degree of anxiety toward it. Moreover, their pre-ATE was significantly correlated with their perception of engineering design and learning performance. These results indicate that engineering design curricula do not necessarily have a positive effect on student ATE. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Curricula

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Application of a Regional Data Set of the Housing Sector for Hydrogen Storage-Supported Energy System Planning

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Energy Engineering 2025年第5期122卷 1755-1770页

作者： Steffen Schedler Michael Bareev-Rudy Stefanie Meilinger Tanja Clees Department of Engineering and Communication(IWK) Institute for TechnologyResource and Energy-efficient Engineering(TREE)Hochschule Bonn-Rhein-SiegUniversity of Applied ScienceSankt Augustin53757Germany International Centre for Sustainable Development(IZNE) Hochschule Bonn-Rhein-SiegUniversity of Applied ScienceSankt Augustin53757Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss BirlinghovenSankt Augustin53754Germany

Germany aims to achieve a national climate-neutral energy system by *** residential sector still accounts for 29%of end energy consumption,with 74%attributed to the direct use of fossil fuels for heating and hot *** order to reduce fossil energy use in the household sector,great efforts are being made to design new energy concepts that expand the use of renewable energies to supply electricity *** is toconvertparts of the natural gas grid to a hydrogen-based gas grid to deliver and store energy for urban quarters of buildings,especially with older building stock where electrification of heat via heat pumps is difficult due to technical,acoustical,and economic reasons.A comprehensive dataset was generated by a bottom-up analysis with open governmental and statistical data to determine regional building types regarding energy demand,solar potential,and existing grid *** buildings’connections to the electricity,gas,and district heating networks are *** this,a representative sample dataset was chosen as input for a newly developed energy system model based on energy flow *** model simulates the interaction of hydrogen generation(HG)(from excess solar energy by electrolysis),storage in a metal-hydride storage(MHS)tank,and hydrogen use in a connected fuel cell(FC),forming a local PVPtGtHP(Photovoltaic Power-to-Gas-to-Heat-and-Power)*** to the seasonal hydrogen storage path(HSP),a battery will complete the system to forma hybrid energy storage system(HESS).Paired with seasonal time series for PV power,electricity and heat demand,and a model for connection to grid infrastructure,the simulation of different hydrogen applications and MHS placements aims to analyze operating times and energy share of the systems’equipment and existing *** method to obtain the data set together with the simulationmodel presented can be used by energy planners for cities,communities,and building developers

关键词： Hydrogen storage hybrid energy storage system simulation housing sector energy share

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Community Engagement and Ecosystem Services for Mine Rehabilitation

Integrative Conservation

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Integrative Conservation 2025年第1期4卷 107-112页

作者： Pan, Yuan Duddigan, Sarah Devanadera, Ma Catriona E. Quierrez, Rico Neil M. Tibbett, Mark Department of Digital Humanities King's College London London United Kingdom Department of Sustainable Land Management School of Agriculture Policy and Development University of Reading Reading United Kingdom Department of Community and Environmental Resource Planning College of Human Ecology University of the Philippines Los Baños Los Baños Philippines Department of Science and Technology Philippine Nuclear Research Institute (DOST-PNRI) Quezon City Philippines

The growing global demand for minerals presents economic opportunities for countries such as the Philippines but also raises concerns about the impacts of mineral extraction on local communities and ecosystem services. This study explores an interdisciplinary community engagement process informing mine rehabilitation in the Philippines, framed through an ecosystem services lens. As part of the PROMT (Philippines Remediation of Mine Tailings) project, we conducted an initial participatory workshop at the Padcal Mine site, engaging Indigenous communities to assess local perspectives on ecosystem services and the impacts of mining activities. Through video presentations, a questionnaire, and focus groups, we found that 88% of participants understood the concept of ecosystem services after viewing an introductory video, while 62% believed mining negatively affected ecosystem services. Interestingly, 65% felt that rehabilitated mines could provide future ecosystem services, such as agriculture or ecotourism. Gender differences were evident, with women perceiving more significant mining impacts but being more optimistic about the rehabilitation potential. This underscores the importance of inclusive engagement approaches. The ecosystem services framework effectively bridges ecological knowledge with community priorities and tangible benefits, promoting social acceptance of mine rehabilitation. This interdisciplinary approach, particularly the integration of Indigenous perspectives, remains underused in mining contexts. Our community-based participatory methodology provides guidance for balancing diverse socioeconomic and environmental considerations. © 2025 The Author(s). Integrative Conservation published by John Wiley & Sons Australia, Ltd on behalf of Xishuangbanna Tropical Botanical Garden (XTBG).

关键词： community engagement ecosystem services mine rehabilitation mine tailings mineral extraction stakeholder engagement

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Multimodal Brain Tumor Segmentation Using a Dual-Branch Vision Transformer Integrated with Region-Attention Fusion Network

Multimodal Brain Tumor Segmentation Using a Dual-Branch Visi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sunita Yadwad Rajeswari C D. Nirmal Raj Pravin Dange Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur India Balaji Institute of Management and Human Resource Development (BIMHRD) Pune India Symbiosis Institute of Management Studies Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Accurate segmentation of brain tumors is a critical task in medical imaging, aiding diagnosis, treatment planning, and prognosis. This paper introduces a novel framework for brain tumor segmentation that leverages a Dual-Branch Vision Transformer (DB-ViT) integrated with a Region-Attention Fusion Network (RAFN) to address the limitations of conventional convolutional neural networks. The DB-ViT processes multimodal MRI sequences, including T1, T1c, T2, and FLAIR, to extract global contextual features while preserving modality-specific information. Simultaneously, the RAFN enhances segmentation precision by refining local features and focusing on critical regions of interest through an attention mechanism. The proposed method effectively combines global and local feature representations, ensuring accurate delineation of tumor boundaries. Extensive experiments were conducted on the BraTS dataset, demonstrating the superiority of our approach over existing methods such as U-Net, Attention U-Net, and TransUNet. Quantitative evaluation metrics, including Dice Score and Intersection over Union (IoU), indicate significant improvements in segmentation accuracy. The framework also achieves computational efficiency and robustness across diverse tumor morphologies. Visual results further validate its ability to enhance boundary delineation, particularly in heterogeneous tumor regions. The proposed methodology represents a significant advancement in automated medical image analysis, offering potential for real-time clinical applications. Future work aims to explore broader clinical validation and real-time deployment of this framework.

关键词： Measurement Image segmentation Computer vision Accuracy Attention mechanisms Refining Brain tumors Transformers Real-time systems Planning

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Enhancing Image Search Engine Accuracy Using Psychological Intention Diagrams

Enhancing Image Search Engine Accuracy Using Psychological I...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Aswathi KP Sunita Yadwad D. Nirmal Raj Shailesh Rastogi Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer science and Engineering Faculty of Engineering and Technology Parul University Vadodara India Balaji Institute of Management and Human Resource Development (BIMHRD) Pune India Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

As the Internet becomes increasingly image-centric, users face substantial challenges in efficiently locating desired images due to the inherent limitations of image search engines in interpreting visual content and keyword ambiguity. This paper introduces an innovative solution employing psychological intention diagrams to enhance search accuracy. These diagrams harness historical data to depict the search intentions associated with specific keywords, reflecting both user and web designer perspectives. By integrating these diagrams, our proposed search engine significantly refines its ability to predict and retrieve the most relevant images. The implementation of this system involves constructing two types of intention diagrams: one based on past user queries and another derived from the intentions of web designers for cases without historical user data. This dual approach not only captures the semantic essence of images more effectively but also addresses the multi-faceted meanings of search keywords. Our experimental results confirm that incorporating psychological dimensions into image search mechanisms substantially improves precision, facilitating more accurate and user-aligned image retrieval. This pioneering approach is the first to apply psychological profiling in enhancing image search engine functionalities, marking a significant advancement in digital image retrieval technologies.

关键词： Visualization Accuracy Digital images Semantics Image retrieval Psychology Machine learning Search engines Internet Standards

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Using Machine Learning to Identify Correlates of Youth Mental Health and Well-Being: A Cross-Sectional Population Study

SSRN

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SSRN 2025年

作者： Li, Lydia M. Lin, Hause Georgiades, Katholiki Wade, Mark Department of Applied Psychology and Human Development University of Toronto TorontoON Canada Sloan School of Management Massachusetts Institute of Technology CambridgeMA United States Offord Centre for Child Studies Departments of Psychiatry and Behavioural Neurosciences McMaster University HamiltonON Canada

Background: Research has identified numerous personal, school, and family factors associated with child mental health, but it remains unclear which are the strongest correlates that contribute to the most accurate prediction of different mental health outcomes and whether they are common across these outcomes or specific to them. Methods: Data were drawn from the 2014-2015 School Mental Health Survey conducted in Canada. Youth self-reported mental well-being using the adapted Mental Health Continuum Short Form and mental health symptoms that encompass internalizing (anxiety, depression) and externalizing (ADHD, CD, ODD) symptoms dimensions using the modified Ontario Child Health Study Emotional Behavioural Scales. We used machine learning-based feature importance analyses to identify the strongest correlates of mental health outcomes. We also fitted generalized random forest models to evaluate gender, parental immigration and educational background group differences in the associations between the strongest common correlates and mental health outcomes. Findings: This study included 24 692 grades 6-12 youth (12 754 [51.7%] girls;11 785 [47.7%] boys;15 528 [62.9%] White). School belonging, common to different mental health outcomes, was the strongest correlate of general mental well-being prediction, whereas friendship quality was specific to it. Lifestyle behaviours (eg, sleep, exercise) were the strongest correlate of the internalizing symptoms dimension, whereas gender and age were specific to this dimension. Class preparedness, school behavioural infractions, and academic achievement were specific to externalizing symptoms. Most correlates of mental health outcomes showed group differences regardless of levels of exposure. For example, while lifestyle behaviours were common to internalizing symptoms of both girls and boys, higher levels were associated with fewer internalizing symptoms among girls than boys. Interpretation: Partially distinct personal, school, and

关键词： Adversarial machine learning

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Scalable Subspace Clustering via Purity Kernel Tensor Learning

Scalable Subspace Clustering via Purity Kernel Tensor Learni...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Rachit Adhvaryu S Rajes Kannan Sanjay C. P Pravin Dange Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur India Balaji Institute of Management and Human Resource Development (BIMHRD) Sri Balaji University Pune India Symbiosis Institute of Management Studies Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Subspace clustering is a fundamental task in machine learning, particularly for high-dimensional datasets where the goal is to group data points that lie in distinct subspaces. Traditional methods, such as Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR), have demonstrated good performance but often suffer from high computational costs, particularly when applied to large-scale data. To address these limitations, this paper introduces a novel technique called Purity Kernel Tensor Learning (PKTL). PKTL combines the power of tensor decomposition with a purity kernel function to improve both the efficiency and effectiveness of subspace clustering. By utilizing tensor-based representations, the method captures higher-order relationships in the data, allowing for a more robust clustering process. The purity kernel function measures the coherence within the clusters, enhancing the identification of subspaces in complex datasets. The proposed approach not only reduces computational complexity but also improves the clustering accuracy compared to existing methods. Experiments on synthetic and real-world datasets, including ORL, Yale, and MNIST, show that PKTL outperforms conventional subspace clustering algorithms, both in terms of clustering accuracy and scalability. The results demonstrate the potential of PKTL in handling large datasets while maintaining high performance, making it a promising approach for scalable subspace clustering in real-world applications.

关键词： Tensors Accuracy Scalability Noise Clustering algorithms Machine learning Robustness Computational efficiency Noise measurement Kernel

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