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
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
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
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
Trafic fow prediction is crucial for intelligent transportation and aids in route planning and navigation.However,existing studies often focus on prediction accuracy improvement,while neglecting external influences an...Trafic fow prediction is crucial for intelligent transportation and aids in route planning and navigation.However,existing studies often focus on prediction accuracy improvement,while neglecting external influences and practical issues like resource constraints and data sparsity on edge devices.We propose an online transfer learning(OTL)framework with a multi-layer perceptron(MLP)-assisted graph convolutional network(GCN),termed OTL-GM,which consists of two parts:transferring source-domain features to edge devices and using online learning to bridge domain gaps.Experiments on four data sets demonstrate OTL's effectiveness;in a comparison with models not using OTL,the reduction in the convergence time of the OTL models ranges from 24.77% to 95.32%.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
In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes consid...In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.展开更多
Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-f...Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.展开更多
The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting me...The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.展开更多
Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification.Although this technique is widely used in multi-label c...Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification.Although this technique is widely used in multi-label classification problems,batch learning deals with most issues,which consumes a lot of time and space resources.Unlike traditional batch learning methods,online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets.However,existing online learning research has done little to consider correlations between labels.On the basis of existing research,this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples.We evaluate the performance of the proposed algorithm on several public datasets.Experiments show the effectiveness of our algorithm.展开更多
Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not al...Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always satisfactory.The technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the users.Factors that affect the Quality of Online Learning Experience(OL-QoE)become more complicated.To get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology theory.Specifically,we first formulate a general definition of OL-QoE by data analysis and experimental verification.This formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect OLQoE.Then,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE prediction.Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity.Finally,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values.Through timely interventions,their OL-QoE and learning performance can be improved.Experimental results verify the effectiveness oftheproposed scheme.展开更多
Over the past few years,China’s higher education institutions have experienced remarkable growth in online teaching.However,it remains uncertain whether and how the sense of presence perceived by students affects the...Over the past few years,China’s higher education institutions have experienced remarkable growth in online teaching.However,it remains uncertain whether and how the sense of presence perceived by students affects their online learning outcomes when teachers use online teaching media for communication.This sense specifically pertains to the extent to which students perceive themselves as“real persons”and establish connections with others.Therefore,this study constructs a conceptual model elucidating the impact of presence on students’online learning outcomes and empirically examines the mechanism through which three types of presence influence students’online learning.The test results of the structural equation modeling(SEM)indicate that:(a)teaching presence,social presence,and cognitive presence all exhibit significantly positive outcomes on students’online learning outcomes;(b)these three types of presence can also indirectly and positively influence students’online learning outcomes through the mediating effect of flow experience and learning satisfaction;and(c)flow experience and learning satisfaction play a sequential mediating role in the process by which presence impacts students’online learning outcomes.We hope that the relevant research findings may contribute to unveiling the“black box”of the impact of presence on students’online learning outcomes and offer valuable insights for college educators to overcome online teaching constraints and enhance online teaching quality.展开更多
[Objectives]To explore the current situation and effects of online learning from the perspective of students,as well as the learning dynamics,and to explore the online teaching methods.It is possible to put forward re...[Objectives]To explore the current situation and effects of online learning from the perspective of students,as well as the learning dynamics,and to explore the online teaching methods.It is possible to put forward relevant suggestions for the problems presented by online teaching,find effective teaching methods,and explore appropriate online teaching methods.[Methods]The nursing students of the 2020,2021,and 2022 grades of Chengde Nursing Vocational College were selected as the research subjects.A self-made questionnaire survey method was adopted.The questionnaire had 5 items and 44 questions:3 questions on personal information,9 questions on the teacher level,12 ques-tions on the student level,7 questions on the technical level,and 13 questions on online learning satisfaction.[Results]In the process of on-line teaching,the cooperation between family and school can be carried out to give full play to the important role of family supervision in online learning and other education,which is conducive to maintaining the discipline of online courses on the Internet,increasing the online learning effect of students,and avoiding the temptation of the Internet to the greatest extent.In the process,we should increase students'self-control in online learning,help students shape realistic goals,and thus improve the effect of online learning.Technical level:increase information-based teaching,enrich teaching content,and introduce virtual simulation software to simulate clinical operations,so as to increase students’interest and enthusiasm in learning.[Conclusions]This study is expected to provide a certain reference for the smooth and efficient development of online teaching and online learning skills.展开更多
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.
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 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Project supported by the National Natural Science Foundation of China(No.62171182)the Natural Science Foundation of Chongqing,the Chongqing Science and Technology Commission(No.CSTB2022NSCQ-MSX0770)+1 种基金the Science and Technology Plan Project of Hunan Provincial Department of Transportation,China(No.202306)the Hunan Emergency Management Science and Technology Project(No.yjtkjxm_202407)。
文摘Trafic fow prediction is crucial for intelligent transportation and aids in route planning and navigation.However,existing studies often focus on prediction accuracy improvement,while neglecting external influences and practical issues like resource constraints and data sparsity on edge devices.We propose an online transfer learning(OTL)framework with a multi-layer perceptron(MLP)-assisted graph convolutional network(GCN),termed OTL-GM,which consists of two parts:transferring source-domain features to edge devices and using online learning to bridge domain gaps.Experiments on four data sets demonstrate OTL's effectiveness;in a comparison with models not using OTL,the reduction in the convergence time of the OTL models ranges from 24.77% to 95.32%.
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supported by the National Natural Science Foundation of China(61771372,61771367,62101494)the National Outstanding Youth Science Fund Project(61525105)+1 种基金Shenzhen Science and Technology Program(KQTD20190929172704911)the Aeronautic al Science Foundation of China(2019200M1001)。
文摘In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.
文摘Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.
基金supported by the National Natural Science Foundation of China (No.U1960202).
文摘The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.
基金Supported by the State Grid Technology Item(52460D230002)。
文摘Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification.Although this technique is widely used in multi-label classification problems,batch learning deals with most issues,which consumes a lot of time and space resources.Unlike traditional batch learning methods,online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets.However,existing online learning research has done little to consider correlations between labels.On the basis of existing research,this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples.We evaluate the performance of the proposed algorithm on several public datasets.Experiments show the effectiveness of our algorithm.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX20_0733)Education Reform Foundation of Jiangsu Province(Grant No.2021JSJG364)+1 种基金Key Education Reform Foundation of NJUPT(Grant No.JG00220JX02,JG00218JX03,JG00215JX01,JG00214JX52)the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always satisfactory.The technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the users.Factors that affect the Quality of Online Learning Experience(OL-QoE)become more complicated.To get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology theory.Specifically,we first formulate a general definition of OL-QoE by data analysis and experimental verification.This formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect OLQoE.Then,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE prediction.Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity.Finally,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values.Through timely interventions,their OL-QoE and learning performance can be improved.Experimental results verify the effectiveness oftheproposed scheme.
基金the project“Research on the Evaluation Mechanism of College Ideological and Political Education:A Perspective on Teacher-Student Development,”funded by Zhejiang Provincial College Ideological and Political Education Research Project.
文摘Over the past few years,China’s higher education institutions have experienced remarkable growth in online teaching.However,it remains uncertain whether and how the sense of presence perceived by students affects their online learning outcomes when teachers use online teaching media for communication.This sense specifically pertains to the extent to which students perceive themselves as“real persons”and establish connections with others.Therefore,this study constructs a conceptual model elucidating the impact of presence on students’online learning outcomes and empirically examines the mechanism through which three types of presence influence students’online learning.The test results of the structural equation modeling(SEM)indicate that:(a)teaching presence,social presence,and cognitive presence all exhibit significantly positive outcomes on students’online learning outcomes;(b)these three types of presence can also indirectly and positively influence students’online learning outcomes through the mediating effect of flow experience and learning satisfaction;and(c)flow experience and learning satisfaction play a sequential mediating role in the process by which presence impacts students’online learning outcomes.We hope that the relevant research findings may contribute to unveiling the“black box”of the impact of presence on students’online learning outcomes and offer valuable insights for college educators to overcome online teaching constraints and enhance online teaching quality.
基金Supported by General Project"Research on the Current Status,Effects and Satisfaction of Online Learning of Nursing Related Courses Based on Students'Perspectives under the Epidemic Situation"(Y202214).
文摘[Objectives]To explore the current situation and effects of online learning from the perspective of students,as well as the learning dynamics,and to explore the online teaching methods.It is possible to put forward relevant suggestions for the problems presented by online teaching,find effective teaching methods,and explore appropriate online teaching methods.[Methods]The nursing students of the 2020,2021,and 2022 grades of Chengde Nursing Vocational College were selected as the research subjects.A self-made questionnaire survey method was adopted.The questionnaire had 5 items and 44 questions:3 questions on personal information,9 questions on the teacher level,12 ques-tions on the student level,7 questions on the technical level,and 13 questions on online learning satisfaction.[Results]In the process of on-line teaching,the cooperation between family and school can be carried out to give full play to the important role of family supervision in online learning and other education,which is conducive to maintaining the discipline of online courses on the Internet,increasing the online learning effect of students,and avoiding the temptation of the Internet to the greatest extent.In the process,we should increase students'self-control in online learning,help students shape realistic goals,and thus improve the effect of online learning.Technical level:increase information-based teaching,enrich teaching content,and introduce virtual simulation software to simulate clinical operations,so as to increase students’interest and enthusiasm in learning.[Conclusions]This study is expected to provide a certain reference for the smooth and efficient development of online teaching and online learning skills.
