To meet the need for cultivating application-oriented talents in local universities,this study introduced a project-based learning approach into the reform of bioinformatics experimental teaching.The course was struct...To meet the need for cultivating application-oriented talents in local universities,this study introduced a project-based learning approach into the reform of bioinformatics experimental teaching.The course was structured around a project titled"Influenza Virus Analysis",comprising four progressive modules:database utilization and information retrieval,sequence alignment and phylogenetic analysis,functional and structural prediction,and omics data analysis.These modules were integrated into a coherent research workflow that connected fragmented knowledge and technical skills.During implementation,flipped classroom and group collaboration methods were employed,alongside the establishment of a diversified assessment system emphasizing process evaluation.Teaching practice indicates that the reform effectively enhances students professional application skills,learning experience,and scientific literacy,facilitating a shift from"tool operation"to"problem-solving"capabilities.This study provides a reference model for the reform of bioinformatics experimental teaching in local universities.展开更多
Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed...Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
In an era defined by complex,interconnected challenges like climate change,pandemics,and resource depletion,the traditional siloed approach to science education is proving increasingly insufficient.Interdisciplinary p...In an era defined by complex,interconnected challenges like climate change,pandemics,and resource depletion,the traditional siloed approach to science education is proving increasingly insufficient.Interdisciplinary project-based learning represents a promising path forward in science education,fostering integrated and holistic learning experiences that move beyond isolated subject learning.Grounded in philosophical ideas of holism,pragmatism,constructivism,and transcendentalism,this article presents a case project illustrating the practical application of interdisciplinary project-based learning.This project engages students in integrating concepts from biology,chemistry,earth science,engineering,and social studies.Through phased activities-research and planning,data collection,implementation,and presentation-students develop a decent understanding of real-world problems while fostering skills in collaboration,problem-solving,and a sense of civic responsibility.Additionally,strategies are proposed to navigate the challenges associated with implementing interdisciplinary project-based learning,including aligning projects with standards,investing in professional development,leveraging community resources,and building support from stakeholders.展开更多
Contemporary higher education prioritizes cultivating students’key competencies and comprehensive problem-solving abilities,specifically fostering innovation,goal orientation,and initiative.This study investigates a ...Contemporary higher education prioritizes cultivating students’key competencies and comprehensive problem-solving abilities,specifically fostering innovation,goal orientation,and initiative.This study investigates a pedagogical framework that synergizes Research-Led Learning(RLL)and Project-Based Learning(PBL)to establish an open,exploratory learning environment.Employing a case study methodology,the research tracked architecture students engaging in a structured PBL process involving rigorous research activities—ranging from theoretical analysis to field investigations—to develop evidence-based design solutions.Evaluations from both student and faculty perspectives assessed the pedagogical effectiveness regarding learning outcomes and competency development.The findings indicate that this methodology effectively bridges the gap between research and practice,significantly bolstering students’capacity to address authentic challenges and propelling self-directed learning in architectural education.展开更多
This study explores a novel educational model of generative AI-empowered interdisciplinary project-based learning(PBL).By analyzing the current applications of generative AI technology in information technology curric...This study explores a novel educational model of generative AI-empowered interdisciplinary project-based learning(PBL).By analyzing the current applications of generative AI technology in information technology curricula,it elucidates its advantages and operational mechanisms in interdisciplinary PBL.Combining case studies and empirical research,the investigation proposes implementation pathways and strategies for the generative AI-enhanced interdisciplinary PBL model,detailing specific applications across three phases:project preparation,implementation,and evaluation.The research demonstrates that generative AI-enabled interdisciplinary project-based learning can effectively enhance students’learning motivation,interdisciplinary thinking capabilities,and innovative competencies,providing new conceptual frameworks and practical approaches for educational model innovation.展开更多
The“Opinions on Comprehensively Deepening Curriculum Reform to Fulfill the Fundamental Task of Strengthening Moral Education”,issued by China’s Ministry of Education in 2015,explicitly identified Project-Based Lear...The“Opinions on Comprehensively Deepening Curriculum Reform to Fulfill the Fundamental Task of Strengthening Moral Education”,issued by China’s Ministry of Education in 2015,explicitly identified Project-Based Learning(PBL)as a key strategy for cultivating students’core competencies.Since then,PBL has been widely implemented as a pilot initiative in primary and secondary schools,gaining increasing influence.Analyzing the intellectual foundations of PBL research in China can offer valuable insights into its theoretical and practical dimensions.This study uses CiteSpace to examine 156 PBL-related articles from the CSSCI database,revealing that the knowledge base of PBL research is primarily built on two major domains.The first is the theoretical foundation,characterized by frequently cited literature focusing on the conceptual framework,educational value,interdisciplinary approaches,core competency cultivation,and instructional objectives of PBL.The second is empirical research,where highly cited studies include case analyses across K–12 settings,general high schools,and higher education institutions.Moving forward,future research on PBL should explore its meaning and value from a dual-subject and integrated perspective,expand case studies to include vocational education,and further promote the interdisciplinary development of core competencies through PBL.展开更多
Online learning has taken root and with the advancement in technology more and more educators are embracing online learning.During the COVID-19 pandemic,with total shut down of face-to-face learning,the Kenyan governm...Online learning has taken root and with the advancement in technology more and more educators are embracing online learning.During the COVID-19 pandemic,with total shut down of face-to-face learning,the Kenyan government had moved learning online and remotely.To find out on how learners in remote areas were experiencing learning during the COVID-19 pandemic,a study was carried out using rapid ethnography design.Five final year secondary students were sampled.Data was collected through interviews,observations,and document analysis.This paper reports on the findings of online and remote learning platforms,which were available,accessed,and preferred by the learners in remote areas of Kenya during the pandemic.It also highlights the importance of e-learning platforms in addressing learning experiences and success.展开更多
Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form ...Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form an empirically validated model,and(3)how they interact within this model,through systematic analysis of 9,207 discussion forum posts from a Chinese University MOOC platform.Results demonstrate that learning drive,course structure,teaching competence,interaction behavior,expected outcomes,and online learning context significantly influence EFL online interactive learning.The analysis reveals two key mechanisms:expected outcomes mediate the effects of learning drive(β=0.45),course structure,teaching competence,and interaction behavior(β=0.35)on learning outcomes,while online learning context moderates these relationships(β=0.25).Specifically,learning drive provides intrinsic/extrinsic motivation,whereas course structure,teaching competence,interaction behavior,and expected outcomes collectively enhance interaction quality and sustainability.These findings,derived through rigorous grounded theory methodology involving open,axial,and selective coding of large-scale interaction data,yield three key contributions:(1)a comprehensive theoretical model of EFL online learning dynamics,(2)empirical validation of mediation/moderation mechanisms,and(3)practical strategies for designing scaffolded interaction protocols and adaptive feedback systems.The study establishes that its theoretically saturated model(achieved after analyzing 7,366 posts with 1,841 verification cases)offers educators evidence-based approaches to optimize collaborative interaction in digital EFL environments.展开更多
In the current trend of educational digitization,online learning platforms have proliferated,making the visual design of digital learning resources increasingly critical.However,existing visual designs for online lear...In the current trend of educational digitization,online learning platforms have proliferated,making the visual design of digital learning resources increasingly critical.However,existing visual designs for online learning resources face numerous challenges.The emergence of AIGC(Artificial Intelligence-Generated Content)technology offers innovative solutions to these issues.This paper explores the application of AIGC technology in enhancing the“new quality productive forces”of visual design for online learning resources.It emphasizes the need to balance technological innovation with humanistic care and highlights the importance of human intervention in the design process.展开更多
To address the widespread challenges of insufficient classroom participation,difficulty maintaining learning interest,and inaccurate learning outcome assessment in existing teaching models,this study introduces a hybr...To address the widespread challenges of insufficient classroom participation,difficulty maintaining learning interest,and inaccurate learning outcome assessment in existing teaching models,this study introduces a hybrid and dual-teacher classroom model.Based on big data and online platforms,this study constructs a music teaching resource system that integrates online micro-lessons,real-time interaction,virtual choirs,collaborative composition,and learning behavior tracking to enhance the openness and personalization of teaching.Through comparative experiments,the study focuses on changes in student knowledge acquisition,musical skills,learning interest,and classroom engagement.Results show that the experimental group significantly outperforms the control group in terms of improvement in knowledge test scores,rhythm and pitch performance,task completion rate,and scores on the learning motivation questionnaire.Differences are particularly prominent in interaction frequency and the expressiveness of the work.The study shows that Group A demonstrates significant improvement in all three dimensions:interest increases from 3.1 to 4.2;autonomous learning motivation increases from 3.0 to 4.1;emotional engagement increases from 3.2 to 4.3,demonstrating strong positive effects.Experimental Group B shows the greatest improvement,with interest increasing from 3.0 to 4.5,autonomous learning motivation from 2.9 to 4.4,and emotional engagement from 3.1 to 4.6,indicating the most significant improvements in learning attitudes and emotions.展开更多
Recent progress in topology optimization(TO)has seen a growing integration of machine learning to accelerate computation.Among these,online learning stands out as a promising strategy for large-scale TO tasks,as it el...Recent progress in topology optimization(TO)has seen a growing integration of machine learning to accelerate computation.Among these,online learning stands out as a promising strategy for large-scale TO tasks,as it eliminates the need for pre-collected training datasets by updating surrogate models dynamically using intermediate optimization data.Stress-constrained lightweight design is an important class of problem with broad engineering relevance.Most existing frameworks use pixel or voxel-based representations and employ the finite element method(FEM)for analysis.The limited continuity across finite elements often compromises the accuracy of stress evaluation.To overcome this limitation,isogeometric analysis is employed as it enables smooth representation of structures and thus more accurate stress computation.However,the complexity of the stress-constrained design problem together with the isogeometric representation results in a large computational cost.This work proposes a multi-grid,single-mesh online learning framework for isogeometric topology optimization(ITO),leveraging the Fourier Neural Operator(FNO)as a surrogate model.Operating entirely within the isogeometric analysis setting,the framework provides smooth geometry representation and precise stress computation,without requiring traditional mesh generation.A localized training approach is employed to enhance scalability,while a multi-grid decomposition scheme incorporates global structural context into local predictions to boost FNO accuracy.By learning the mapping from spatial features to sensitivity fields,the framework enables efficient single-resolution optimization,avoiding the computational burden of two-resolution simulations.The proposed method is validated through 2D stress-constrained design examples,and the effect of key parameters is studied.展开更多
6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,faul...6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.展开更多
This study focuses on exploring the practical path of Micro Project-Based Learning from the perspective of integrated unit teaching,aiming to address the issue of integrating the Project Section with other sections in...This study focuses on exploring the practical path of Micro Project-Based Learning from the perspective of integrated unit teaching,aiming to address the issue of integrating the Project Section with other sections in the new junior high school English textbook published by Shanghai Education Press.Based on two rounds of action research,a micro-project design framework is constructed,which includes“unit micro-project design”,“micro-project-based unit teaching”,“micro-project achievement display”,and“evaluation and reflection”.Practice shows that with the guidance of task lists and scaffolding support,this framework effectively promotes the integration of subject knowledge and the development of students’core competence,providing a transferable implementation paradigm for integrated unit teaching.展开更多
The rapid shift to online education has introduced significant challenges to maintaining academic integrity in remote assessments,as traditional proctoring methods fall short in preventing cheating.The increase in che...The rapid shift to online education has introduced significant challenges to maintaining academic integrity in remote assessments,as traditional proctoring methods fall short in preventing cheating.The increase in cheating during online exams highlights the need for efficient,adaptable detection models to uphold academic credibility.This paper presents a comprehensive analysis of various deep learning models for cheating detection in online proctoring systems,evaluating their accuracy,efficiency,and adaptability.We benchmark several advanced architectures,including EfficientNet,MobileNetV2,ResNet variants and more,using two specialized datasets(OEP and OP)tailored for online proctoring contexts.Our findings reveal that EfficientNetB1 and YOLOv5 achieve top performance on the OP dataset,with EfficientNetB1 attaining a peak accuracy of 94.59% and YOLOv5 reaching a mean average precision(mAP@0.5)of 98.3%.For the OEP dataset,ResNet50-CBAM,YOLOv5 and EfficientNetB0 stand out,with ResNet50-CBAMachieving an accuracy of 93.61% and EfficientNetB0 showing robust detection performance with balanced accuracy and computational efficiency.These results underscore the importance of selectingmodels that balance accuracy and efficiency,supporting scalable,effective cheating detection in online assessments.展开更多
An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontol...An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontology and user ontology, and evaluated from its function and implementation; then the corresponding class diagram, resource description framework （RDF） schema and extensible markup language （XML） schema are given. Secondly, the metadata for online learning resource repository management is proposed based on the Dublin Core Metadata Initiative and the IEEE Learning Technologies Standards Committee Learning Object Metadata Working Group. Finally, the inference instance is shown, which proves the validity of ontology and metadata in online learning resource repository management.展开更多
Topological information is very important for understanding different types of online web services,in particular,for online social networks(OSNs).People leverage such information for various applications,such as socia...Topological information is very important for understanding different types of online web services,in particular,for online social networks(OSNs).People leverage such information for various applications,such as social relationship modeling,community detection,user profiling,and user behavior prediction.However,the leak of such information will also pose severe challenges for user privacy preserving due to its usefulness in characterizing users.Large-scale web crawling-based information probing is a representative way for obtaining topological information of online web services.In this paper,we explore how to defend against topological information probing for online web services,with a particular focus on online decentralized web services such as Mastodon.Different from traditional centralized web services,the federated nature of decentralized web services makes the identification of distributed crawlers even more difficult.We analyze the behavioral differences between legitimate users and crawlers in decentralized web services and highlight two key behavioral attributes that distinguish crawlers from legitimate users:instance interaction preferences and hop count in profile viewing patterns.Based on these insights:we propose a supervised machine learning-based framework for crawler detection,which is able to learn the federation-aware feature representations for users.To validate the framework’s effectiveness,we construct a labeled dataset that integrates real users with real-trace driven simulated crawlers in Mastodon.We use this dataset to train various supervised classifiers for crawler detection.Experimental results demonstrate that our framework can achieve an excellent classification performance.Moreover,it is observed that federation-aware features are effective in improving detection performance.展开更多
This study focuses on the effectiveness of the project-based language learning(PBLL) in a college Secretarial Oral English(SOE) Module. Student reflections of the language project work have been analyzed through Activ...This study focuses on the effectiveness of the project-based language learning(PBLL) in a college Secretarial Oral English(SOE) Module. Student reflections of the language project work have been analyzed through Activity Theory. Moreover,Data has been collected and categorized based on the components of complex human activity: the subject, object, tools(signs,symbols, and language), the community in which the activity take place, division of labor, and rules. The findings theoretically support the outcome of project-based language learning which align with the object of the activity.展开更多
This study aims to explore Chinese university EFL learners'perceptions toward alternative assessment in a context of a project-based learning digital storytelling presentation in Speaking Course.It also seeks to c...This study aims to explore Chinese university EFL learners'perceptions toward alternative assessment in a context of a project-based learning digital storytelling presentation in Speaking Course.It also seeks to compare the relationship between alternative assessment and teacher assessment.The findings showed that a strong correlation between alternative assessment and teacher assessment occurred.Alternative assessment activities are viewed by students as"authentic"assessments,as they mimic how the student will be using their knowledge in the future.Alternative assessment as a form of formative assessment can be a powerful day-to-day tool for teachers and students.Alternative assessment is an enabler of process of learning.The study suggests that alternative assessment can encourage learners to become more fully responsible for their learning and can result in more and better learning.Alternative assessment can thus be used as a golden key to the"deaf and dumb"phenomenon for Chinese university EFL learners.展开更多
This paper examines the strategies of developing online learning in Chinese universities.Top-down strategies include policy,funding,Senior initiative and task-based management,etc,in which funding generally plays the ...This paper examines the strategies of developing online learning in Chinese universities.Top-down strategies include policy,funding,Senior initiative and task-based management,etc,in which funding generally plays the most important role followed by Senior initiative and task-based management.Bottom-up strategies,especially staff training and contest are often seen as essential to successfully improve online learning.展开更多
基金Supported by Undergraduate Higher Education Teaching Quality and Reform Projects of Guangdong Province(Yuejiao Gao Han[2024]No.9,Yuejiao Gao Han[2024]No.30)Guangdong Basic and Applied Basic Research Foundation(2023A1515110973)+1 种基金Guangdong Provincial Young Innovative Talents Project of General Colleges and Universities(2023KQNCX089)Quality Engineering and Teaching Reform Projects of Zhaoqing University(zlgc202239,zlgc202207,zlgc2024005,zlgc2024038).
文摘To meet the need for cultivating application-oriented talents in local universities,this study introduced a project-based learning approach into the reform of bioinformatics experimental teaching.The course was structured around a project titled"Influenza Virus Analysis",comprising four progressive modules:database utilization and information retrieval,sequence alignment and phylogenetic analysis,functional and structural prediction,and omics data analysis.These modules were integrated into a coherent research workflow that connected fragmented knowledge and technical skills.During implementation,flipped classroom and group collaboration methods were employed,alongside the establishment of a diversified assessment system emphasizing process evaluation.Teaching practice indicates that the reform effectively enhances students professional application skills,learning experience,and scientific literacy,facilitating a shift from"tool operation"to"problem-solving"capabilities.This study provides a reference model for the reform of bioinformatics experimental teaching in local universities.
基金Science and Technology Development Program of the“Taihu Light”(K20231023)CMA Numerical Weather Prediction R&D Project(TCYF2024QH007)+1 种基金“Qing Lan”Project of Jiangsu Province for C.H.LUWuxi University Research Start-up Fund for Introduced Talents(2023r037)。
文摘Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported by the Anhui Provincial Education Science Research Program titled“Research on the Construction and Application of Evaluation Frameworks for Interdisciplinary Practical Activities in Primary School Science”(JKT25114)the Humanities and Social Sciences Research Program of Anhui Higher Education Institutions(2022AH052117).
文摘In an era defined by complex,interconnected challenges like climate change,pandemics,and resource depletion,the traditional siloed approach to science education is proving increasingly insufficient.Interdisciplinary project-based learning represents a promising path forward in science education,fostering integrated and holistic learning experiences that move beyond isolated subject learning.Grounded in philosophical ideas of holism,pragmatism,constructivism,and transcendentalism,this article presents a case project illustrating the practical application of interdisciplinary project-based learning.This project engages students in integrating concepts from biology,chemistry,earth science,engineering,and social studies.Through phased activities-research and planning,data collection,implementation,and presentation-students develop a decent understanding of real-world problems while fostering skills in collaboration,problem-solving,and a sense of civic responsibility.Additionally,strategies are proposed to navigate the challenges associated with implementing interdisciplinary project-based learning,including aligning projects with standards,investing in professional development,leveraging community resources,and building support from stakeholders.
基金received approval from a committee named Innovation Institute for Sustainable Maritime Architecture Research and Technology(iSMART)(The certificate number was 2022-5-22-01).
文摘Contemporary higher education prioritizes cultivating students’key competencies and comprehensive problem-solving abilities,specifically fostering innovation,goal orientation,and initiative.This study investigates a pedagogical framework that synergizes Research-Led Learning(RLL)and Project-Based Learning(PBL)to establish an open,exploratory learning environment.Employing a case study methodology,the research tracked architecture students engaging in a structured PBL process involving rigorous research activities—ranging from theoretical analysis to field investigations—to develop evidence-based design solutions.Evaluations from both student and faculty perspectives assessed the pedagogical effectiveness regarding learning outcomes and competency development.The findings indicate that this methodology effectively bridges the gap between research and practice,significantly bolstering students’capacity to address authentic challenges and propelling self-directed learning in architectural education.
文摘This study explores a novel educational model of generative AI-empowered interdisciplinary project-based learning(PBL).By analyzing the current applications of generative AI technology in information technology curricula,it elucidates its advantages and operational mechanisms in interdisciplinary PBL.Combining case studies and empirical research,the investigation proposes implementation pathways and strategies for the generative AI-enhanced interdisciplinary PBL model,detailing specific applications across three phases:project preparation,implementation,and evaluation.The research demonstrates that generative AI-enabled interdisciplinary project-based learning can effectively enhance students’learning motivation,interdisciplinary thinking capabilities,and innovative competencies,providing new conceptual frameworks and practical approaches for educational model innovation.
基金Provincial-Level Quality Engineering Project,Preschool Education Teacher Training Base of Fuyang Normal University(Project No.:2023cyts023)University-Level Research Team Project,Collaborative Innovation Center for Basic Education in Northern Anhui(Project No.:kytd202418)。
文摘The“Opinions on Comprehensively Deepening Curriculum Reform to Fulfill the Fundamental Task of Strengthening Moral Education”,issued by China’s Ministry of Education in 2015,explicitly identified Project-Based Learning(PBL)as a key strategy for cultivating students’core competencies.Since then,PBL has been widely implemented as a pilot initiative in primary and secondary schools,gaining increasing influence.Analyzing the intellectual foundations of PBL research in China can offer valuable insights into its theoretical and practical dimensions.This study uses CiteSpace to examine 156 PBL-related articles from the CSSCI database,revealing that the knowledge base of PBL research is primarily built on two major domains.The first is the theoretical foundation,characterized by frequently cited literature focusing on the conceptual framework,educational value,interdisciplinary approaches,core competency cultivation,and instructional objectives of PBL.The second is empirical research,where highly cited studies include case analyses across K–12 settings,general high schools,and higher education institutions.Moving forward,future research on PBL should explore its meaning and value from a dual-subject and integrated perspective,expand case studies to include vocational education,and further promote the interdisciplinary development of core competencies through PBL.
文摘Online learning has taken root and with the advancement in technology more and more educators are embracing online learning.During the COVID-19 pandemic,with total shut down of face-to-face learning,the Kenyan government had moved learning online and remotely.To find out on how learners in remote areas were experiencing learning during the COVID-19 pandemic,a study was carried out using rapid ethnography design.Five final year secondary students were sampled.Data was collected through interviews,observations,and document analysis.This paper reports on the findings of online and remote learning platforms,which were available,accessed,and preferred by the learners in remote areas of Kenya during the pandemic.It also highlights the importance of e-learning platforms in addressing learning experiences and success.
文摘Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form an empirically validated model,and(3)how they interact within this model,through systematic analysis of 9,207 discussion forum posts from a Chinese University MOOC platform.Results demonstrate that learning drive,course structure,teaching competence,interaction behavior,expected outcomes,and online learning context significantly influence EFL online interactive learning.The analysis reveals two key mechanisms:expected outcomes mediate the effects of learning drive(β=0.45),course structure,teaching competence,and interaction behavior(β=0.35)on learning outcomes,while online learning context moderates these relationships(β=0.25).Specifically,learning drive provides intrinsic/extrinsic motivation,whereas course structure,teaching competence,interaction behavior,and expected outcomes collectively enhance interaction quality and sustainability.These findings,derived through rigorous grounded theory methodology involving open,axial,and selective coding of large-scale interaction data,yield three key contributions:(1)a comprehensive theoretical model of EFL online learning dynamics,(2)empirical validation of mediation/moderation mechanisms,and(3)practical strategies for designing scaffolded interaction protocols and adaptive feedback systems.The study establishes that its theoretically saturated model(achieved after analyzing 7,366 posts with 1,841 verification cases)offers educators evidence-based approaches to optimize collaborative interaction in digital EFL environments.
文摘In the current trend of educational digitization,online learning platforms have proliferated,making the visual design of digital learning resources increasingly critical.However,existing visual designs for online learning resources face numerous challenges.The emergence of AIGC(Artificial Intelligence-Generated Content)technology offers innovative solutions to these issues.This paper explores the application of AIGC technology in enhancing the“new quality productive forces”of visual design for online learning resources.It emphasizes the need to balance technological innovation with humanistic care and highlights the importance of human intervention in the design process.
文摘To address the widespread challenges of insufficient classroom participation,difficulty maintaining learning interest,and inaccurate learning outcome assessment in existing teaching models,this study introduces a hybrid and dual-teacher classroom model.Based on big data and online platforms,this study constructs a music teaching resource system that integrates online micro-lessons,real-time interaction,virtual choirs,collaborative composition,and learning behavior tracking to enhance the openness and personalization of teaching.Through comparative experiments,the study focuses on changes in student knowledge acquisition,musical skills,learning interest,and classroom engagement.Results show that the experimental group significantly outperforms the control group in terms of improvement in knowledge test scores,rhythm and pitch performance,task completion rate,and scores on the learning motivation questionnaire.Differences are particularly prominent in interaction frequency and the expressiveness of the work.The study shows that Group A demonstrates significant improvement in all three dimensions:interest increases from 3.1 to 4.2;autonomous learning motivation increases from 3.0 to 4.1;emotional engagement increases from 3.2 to 4.3,demonstrating strong positive effects.Experimental Group B shows the greatest improvement,with interest increasing from 3.0 to 4.5,autonomous learning motivation from 2.9 to 4.4,and emotional engagement from 3.1 to 4.6,indicating the most significant improvements in learning attitudes and emotions.
基金supported by the Hong Kong Research Grants under Competitive Earmarked Research Grant No.16206320.
文摘Recent progress in topology optimization(TO)has seen a growing integration of machine learning to accelerate computation.Among these,online learning stands out as a promising strategy for large-scale TO tasks,as it eliminates the need for pre-collected training datasets by updating surrogate models dynamically using intermediate optimization data.Stress-constrained lightweight design is an important class of problem with broad engineering relevance.Most existing frameworks use pixel or voxel-based representations and employ the finite element method(FEM)for analysis.The limited continuity across finite elements often compromises the accuracy of stress evaluation.To overcome this limitation,isogeometric analysis is employed as it enables smooth representation of structures and thus more accurate stress computation.However,the complexity of the stress-constrained design problem together with the isogeometric representation results in a large computational cost.This work proposes a multi-grid,single-mesh online learning framework for isogeometric topology optimization(ITO),leveraging the Fourier Neural Operator(FNO)as a surrogate model.Operating entirely within the isogeometric analysis setting,the framework provides smooth geometry representation and precise stress computation,without requiring traditional mesh generation.A localized training approach is employed to enhance scalability,while a multi-grid decomposition scheme incorporates global structural context into local predictions to boost FNO accuracy.By learning the mapping from spatial features to sensitivity fields,the framework enables efficient single-resolution optimization,avoiding the computational burden of two-resolution simulations.The proposed method is validated through 2D stress-constrained design examples,and the effect of key parameters is studied.
基金supported in part by the National Key Research and Development Project under Grant 2020YFB1806805partially funded through a grant from Qualcomm。
文摘6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.
基金this paper is funded by Project Information:2023 Guangdong Undergraduate Colleges and Universities Teaching Quality and Teaching Reform Project Construction Project,Project Name:Action Research on Whole-area Nurturing of English Reading Teaching in Universities,Secondary and Primary Schools under the Perspective of Discipline Nurturing.Project serial number:895.
文摘This study focuses on exploring the practical path of Micro Project-Based Learning from the perspective of integrated unit teaching,aiming to address the issue of integrating the Project Section with other sections in the new junior high school English textbook published by Shanghai Education Press.Based on two rounds of action research,a micro-project design framework is constructed,which includes“unit micro-project design”,“micro-project-based unit teaching”,“micro-project achievement display”,and“evaluation and reflection”.Practice shows that with the guidance of task lists and scaffolding support,this framework effectively promotes the integration of subject knowledge and the development of students’core competence,providing a transferable implementation paradigm for integrated unit teaching.
基金funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R752),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The rapid shift to online education has introduced significant challenges to maintaining academic integrity in remote assessments,as traditional proctoring methods fall short in preventing cheating.The increase in cheating during online exams highlights the need for efficient,adaptable detection models to uphold academic credibility.This paper presents a comprehensive analysis of various deep learning models for cheating detection in online proctoring systems,evaluating their accuracy,efficiency,and adaptability.We benchmark several advanced architectures,including EfficientNet,MobileNetV2,ResNet variants and more,using two specialized datasets(OEP and OP)tailored for online proctoring contexts.Our findings reveal that EfficientNetB1 and YOLOv5 achieve top performance on the OP dataset,with EfficientNetB1 attaining a peak accuracy of 94.59% and YOLOv5 reaching a mean average precision(mAP@0.5)of 98.3%.For the OEP dataset,ResNet50-CBAM,YOLOv5 and EfficientNetB0 stand out,with ResNet50-CBAMachieving an accuracy of 93.61% and EfficientNetB0 showing robust detection performance with balanced accuracy and computational efficiency.These results underscore the importance of selectingmodels that balance accuracy and efficiency,supporting scalable,effective cheating detection in online assessments.
基金The Advanced University Action Plan of the Minis-try of Education of China (2004XD-03).
文摘An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontology and user ontology, and evaluated from its function and implementation; then the corresponding class diagram, resource description framework （RDF） schema and extensible markup language （XML） schema are given. Secondly, the metadata for online learning resource repository management is proposed based on the Dublin Core Metadata Initiative and the IEEE Learning Technologies Standards Committee Learning Object Metadata Working Group. Finally, the inference instance is shown, which proves the validity of ontology and metadata in online learning resource repository management.
基金funded by the National Key R&D Program of China under Grant(No.2022YFB3102901)National Natural Science Foundation of China(No.62072115,No.62102094)Shanghai Science and Technology Innovation Action Plan Project(No.22510713600).
文摘Topological information is very important for understanding different types of online web services,in particular,for online social networks(OSNs).People leverage such information for various applications,such as social relationship modeling,community detection,user profiling,and user behavior prediction.However,the leak of such information will also pose severe challenges for user privacy preserving due to its usefulness in characterizing users.Large-scale web crawling-based information probing is a representative way for obtaining topological information of online web services.In this paper,we explore how to defend against topological information probing for online web services,with a particular focus on online decentralized web services such as Mastodon.Different from traditional centralized web services,the federated nature of decentralized web services makes the identification of distributed crawlers even more difficult.We analyze the behavioral differences between legitimate users and crawlers in decentralized web services and highlight two key behavioral attributes that distinguish crawlers from legitimate users:instance interaction preferences and hop count in profile viewing patterns.Based on these insights:we propose a supervised machine learning-based framework for crawler detection,which is able to learn the federation-aware feature representations for users.To validate the framework’s effectiveness,we construct a labeled dataset that integrates real users with real-trace driven simulated crawlers in Mastodon.We use this dataset to train various supervised classifiers for crawler detection.Experimental results demonstrate that our framework can achieve an excellent classification performance.Moreover,it is observed that federation-aware features are effective in improving detection performance.
文摘This study focuses on the effectiveness of the project-based language learning(PBLL) in a college Secretarial Oral English(SOE) Module. Student reflections of the language project work have been analyzed through Activity Theory. Moreover,Data has been collected and categorized based on the components of complex human activity: the subject, object, tools(signs,symbols, and language), the community in which the activity take place, division of labor, and rules. The findings theoretically support the outcome of project-based language learning which align with the object of the activity.
文摘This study aims to explore Chinese university EFL learners'perceptions toward alternative assessment in a context of a project-based learning digital storytelling presentation in Speaking Course.It also seeks to compare the relationship between alternative assessment and teacher assessment.The findings showed that a strong correlation between alternative assessment and teacher assessment occurred.Alternative assessment activities are viewed by students as"authentic"assessments,as they mimic how the student will be using their knowledge in the future.Alternative assessment as a form of formative assessment can be a powerful day-to-day tool for teachers and students.Alternative assessment is an enabler of process of learning.The study suggests that alternative assessment can encourage learners to become more fully responsible for their learning and can result in more and better learning.Alternative assessment can thus be used as a golden key to the"deaf and dumb"phenomenon for Chinese university EFL learners.
文摘This paper examines the strategies of developing online learning in Chinese universities.Top-down strategies include policy,funding,Senior initiative and task-based management,etc,in which funding generally plays the most important role followed by Senior initiative and task-based management.Bottom-up strategies,especially staff training and contest are often seen as essential to successfully improve online learning.
