Purpose:This research addresses the challenge of concept drift in AI-enabled software,particularly within autonomous vehicle systems where concept drift in object recognition(like pedestrian detection)can lead to misc...Purpose:This research addresses the challenge of concept drift in AI-enabled software,particularly within autonomous vehicle systems where concept drift in object recognition(like pedestrian detection)can lead to misclassifications and safety risks.This study introduces a proactive framework to detect early signs of domain-specific concept drift by leveraging domain analysis and natural language processing techniques.This method is designed to help maintain the relevance of domain knowledge and prevent potential failures in AI systems due to evolving concept definitions.Design/methodology/approach:The proposed framework integrates natural language processing and image analysis to continuously update and monitor key domain concepts against evolving external data sources,such as social media and news.By identifying terms and features closely associated with core concepts,the system anticipates and flags significant changes.This was tested in the automotive domain on the pedestrian concept,where the framework was evaluated for its capacity to detect shifts in the recognition of pedestrians,particularly during events like Halloween and specific car accidents.Findings:The framework demonstrated an ability to detect shifts in the domain concept of pedestrians,as evidenced by contextual changes around major events.While it successfully identified pedestrian-related drift,the system’s accuracy varied when overlapping with larger social events.The results indicate the model’s potential to foresee relevant shifts before they impact autonomous systems,although further refinement is needed to handle high-impact concurrent events.Research limitations:This study focused on detecting concept drift in the pedestrian domain within autonomous vehicles,with results varying across domains.To assess generalizability,we tested the framework for airplane-related incidents and demonstrated adaptability.However,unpredictable events and data biases from social media and news may obscure domain-specific drifts.Further evaluation across diverse applications is needed to enhance robustness in evolving AI environments.Practical implications:The proactive detection of concept drift has significant implications for AI-driven domains,especially in safety-critical applications like autonomous driving.By identifying early signs of drift,this framework provides actionable insights for AI system updates,potentially reducing misclassification risks and enhancing public safety.Moreover,it enables timely interventions,reducing costly and labor-intensive retraining requirements by focusing only on the relevant aspects of evolving concepts.This method offers a streamlined approach for maintaining AI system performance in environments where domain knowledge rapidly changes.Originality/value:This study contributes a novel domain-agnostic framework that combines natural language processing with image analysis to predict concept drift early.This unique approach,which is focused on real-time data sources,offers an effective and scalable solution for addressing the evolving nature of domain-specific concepts in AI applications.展开更多
In recent years,artificial intelligence(AI)has been increasingly integrated into educational settings worldwide.This study aims to explore the effectiveness of AI classroom teaching for Chinese undergraduate students,...In recent years,artificial intelligence(AI)has been increasingly integrated into educational settings worldwide.This study aims to explore the effectiveness of AI classroom teaching for Chinese undergraduate students,focusing on its influence on learning outcomes and student engagement.The research uses a quantitative approach,utilizing surveys and academic performance data to evaluate two main objectives:(1)the impact of AI teaching methods on academic performance compared to traditional instruction;(2)the level of student engagement and satisfaction with AI-based learning tools.The study sample includes undergraduate students from multiple universities in China,allowing for a diverse representation of various disciplines.Data will be collected through standardized tests,questionnaires,and academic records,ensuring the reliability and validity of the results.The findings will provide insights into the potential advantages and challenges of AI integration in higher education and inform future strategies for adopting AI in Chinese classrooms.By exploring both the academic and practical aspects of AI-driven education,this research aims to contribute valuable knowledge to the growing field of AI in education,particularly in the context of Chinese higher education.The results are expected to have implications for educators,policymakers,and AI developers interested in enhancing the effectiveness of educational technologies.展开更多
Spectroscopy,especially for plasma spectroscopy,provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability.Artificial intelligence(AI)has the tremendou...Spectroscopy,especially for plasma spectroscopy,provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability.Artificial intelligence(AI)has the tremendous potential to build a universal quantitative framework covering all branches of plasma spectroscopy based on its unmatched representation and generalization ability.Herein,we introduce an AI-based unified method called self-supervised image-spectrum twin information fusion detection(SISTIFD)to collect twin co-occurrence signals of the plasma and to intelligently predict the physical parameters for improving the performances of all plasma spectroscopic techniques.It can fuse the spectra and plasma images in synchronization,derive the plasma parameters(total number density,plasma temperature,electron density,and other implicit factors),and provide accurate results.The experimental data demonstrate their excellent utility and capacity,with a reduction of 98%in evaluation indices(root mean square error,relative standard deviation,etc.)and an analysis frequency of 143 Hz(much faster than the mainstream detection frame rate of 1 Hz).In addition,as a completely end-to-end and self-supervised framework,the SISTIFD enables automatic detection without manual preprocessing or intervention.With these advantages,it has remarkably enhanced various plasma spectroscopic techniques with state-of-the-art performance and unsealed their possibility in industry,especially in the regions that require both capability and efficiency.This scheme brings new inspiration to the whole field of plasma spectroscopy and enables in situ analysis with a real-world scenario of high throughput,cross-interference,various analyte complexity,and diverse applications.展开更多
This paper explains how the optimized classrooms were selected and the results that were achieved by the optimizations carried out and finalized.The context of the research is the city of Concepción,in Chile.Virt...This paper explains how the optimized classrooms were selected and the results that were achieved by the optimizations carried out and finalized.The context of the research is the city of Concepción,in Chile.Virtual models of classrooms were evaluated using the Radiance software.We used a methodology that allowed us to determine the luminous conditions under different types of skies,seasons of the year and times of the day.The evaluation of the typologies was performed based on three defined criteria,in order to achieve the stated design objectives.We defined the optimal solutions for each orientation and,finally,we stated design recommendations for daylit classrooms to ensure the visual comfort of the students.These recommendations link all that found in the initial analysis with that found in the optimization stage.展开更多
Surgical nursing education has long grappled with a tension between traditional lecture-based models and actual practice needs.Students have difficulty converting theory into functional nursing skills in the passive f...Surgical nursing education has long grappled with a tension between traditional lecture-based models and actual practice needs.Students have difficulty converting theory into functional nursing skills in the passive format of knowledge acquisition.As medical education concepts have been renewed,the goals of educating nursing talents with independent judgment and adaptability to clinical practice have evolved into a significant direction of teaching reform.To this end,the“flipped classroom”model that blends lecture with discussion,through the mechanism of“internalized absorption”and“discussion”in the classroom,represents both a new teaching model that meets the need for integration of surgical nursing knowledge and an approach to skill application.This model reconstructs the teaching and learning process by providing a bridge for students to transfer from understanding knowledge to clinical thinking,thus providing a path forward for the dilemma of the separation of theory and practice in surgical nursing education.展开更多
With the evolution of society,education reform continues to progress,and increasing attention is being paid to the learning outcomes and overall academic quality of junior high school students.As a core subject at thi...With the evolution of society,education reform continues to progress,and increasing attention is being paid to the learning outcomes and overall academic quality of junior high school students.As a core subject at this stage,the effectiveness of Chinese language teaching directly influences students’overall academic performance.Therefore,enhancing the effectiveness of junior high school Chinese classroom teaching has become a key focus for educators.Against this backdrop,this paper explores the significance of building an effective classroom environment in junior high school Chinese teaching and provides an in-depth analysis of strategies to improve teaching effectiveness.The goal is to assist teachers in creating efficient classrooms,fostering students’interest in learning,enhancing their overall competence in Chinese,and ultimately improving teaching quality to contribute to the overall advancement of Chinese language education.展开更多
The widespread adoption of the internet has provided new platforms and possibilities for Chinese language instruction.Students can utilize online resources or mobile devices for learning outside the classroom,while te...The widespread adoption of the internet has provided new platforms and possibilities for Chinese language instruction.Students can utilize online resources or mobile devices for learning outside the classroom,while teachers can shift the“intensive instruction”component of comprehensive Chinese courses to extracurricular settings.This approach enables increased practice time during class sessions,truly placing the learner at the center of the educational process.The flipped classroom model aligns with this philosophy and complements the disciplinary characteristics of comprehensive Chinese courses.In practice,implementing the O-PIRTAS universal flipped classroom model revealed its effectiveness in enhancing oral proficiency and overall competency.However,it is essential to concurrently address students’writing skills and cultivate their awareness of the flipped classroom approach.展开更多
In recent years,with the continuous development of educational digitalization,the integration model of SPOC(Small Private Online Course)and flipped classroom can integrate online teaching resources and offline teachin...In recent years,with the continuous development of educational digitalization,the integration model of SPOC(Small Private Online Course)and flipped classroom can integrate online teaching resources and offline teaching resources,overcome the shortcomings of traditional teaching models,and enhance students’enthusiasm for learning and autonomous learning ability.This model has become a popular direction in the reform of teaching models.Meanwhile,the advantages of the Zhihuishu knowledge graph platform,such as knowledge coherence and complete teaching modules,are highly beneficial for the promotion of the flipped classroom teaching model based on SPOC.This research will adopt methods such as literature review,investigation and research,questionnaire survey,and in-depth interview to analyze the application effect and optimization path of the flipped classroom teaching model based on SPOC on the Zhihuishu platform in college English teaching.The theoretical basis,practical effect and existing problems of the model were specifically analyzed,and corresponding optimization suggestions were put forward.展开更多
Objective:To explore the application effect of group cooperative learning combined with flipped classroom in standardized training of emergency surgery resident physicians.Methods:95 resident physicians undergoing eme...Objective:To explore the application effect of group cooperative learning combined with flipped classroom in standardized training of emergency surgery resident physicians.Methods:95 resident physicians undergoing emergency surgery standardized training in our hospital were randomly divided into an experimental group(46,group cooperative learning+flipped classroom)and a control group(49,traditional teaching).The training period was 2 months.Results:The graduation assessment scores of the experimental group were higher than those of the control group(P<0.05).The improvement in critical thinking ability and self-learning ability was better than that of the control group(P<0.05).The response rate of“yes”to all items of course teaching satisfaction was significantly higher than that of the control group(P<0.05).The satisfaction scores for training teachers were higher than those of the control group(P<0.05).Conclusion:The teaching model of group cooperative learning combined with flipped classroom,through reconstructing the learning process and activating interactive participation,can significantly improve the clinical operation and theoretical foundation of emergency surgery standardized training students.It also cultivates core professional qualities such as critical thinking,self-learning,and team collaboration,providing a feasible paradigm for integrated medical education in this discipline.展开更多
This paper focuses on the research on the teaching efficiency of ideological and political classrooms under the background of blended teaching.It analyzes the connotation and characteristics of blended teaching,explor...This paper focuses on the research on the teaching efficiency of ideological and political classrooms under the background of blended teaching.It analyzes the connotation and characteristics of blended teaching,explores the impact of blended teaching on the teaching efficiency of ideological and political classrooms from multiple aspects,and conducts empirical research through case analysis and data collection.The results show that blended teaching can effectively improve the teaching efficiency of ideological and political classrooms,enhance students’learning enthusiasm and participation,and promote the improvement of students’ideological and political qualities.Finally,corresponding suggestions and countermeasures are put forward to provide a reference for the improvement of the teaching quality of ideological and political courses.展开更多
The task of student action recognition in the classroom is to precisely capture and analyze the actions of students in classroom videos,providing a foundation for realizing intelligent and accurate teaching.However,th...The task of student action recognition in the classroom is to precisely capture and analyze the actions of students in classroom videos,providing a foundation for realizing intelligent and accurate teaching.However,the complex nature of the classroom environment has added challenges and difficulties in the process of student action recognition.In this research article,with regard to the circumstances where students are prone to be occluded and classroom computing resources are restricted in real classroom scenarios,a lightweight multi-modal fusion action recognition approach is put forward.This proposed method is capable of enhancing the accuracy of student action recognition while concurrently diminishing the number of parameters of the model and the Computation Amount,thereby achieving a more efficient and accurate recognition performance.In the feature extraction stage,this method fuses the keypoint heatmap with the RGB(Red-Green-Blue color model)image.In order to fully utilize the unique information of different modalities for feature complementarity,a Feature Fusion Module(FFE)is introduced.The FFE encodes and fuses the unique features of the two modalities during the feature extraction process.This fusion strategy not only achieves fusion and complementarity between modalities,but also improves the overall model performance.Furthermore,to reduce the computational load and parameter scale of the model,we use keypoint information to crop RGB images.At the same time,the first three networks of the lightweight feature extraction network X3D are used to extract dual-branch features.These methods significantly reduce the computational load and parameter scale.The number of parameters of the model is 1.40 million,and the computation amount is 5.04 billion floating-point operations per second(GFLOPs),achieving an efficient lightweight design.In the Student Classroom Action Dataset(SCAD),the accuracy of the model is 88.36%.In NTU 60(Nanyang Technological University Red-Green-Blue-Depth RGB+Ddataset with 60 categories),the accuracies on X-Sub(The people in the training set are different from those in the test set)and X-View(The perspectives of the training set and the test set are different)are 95.76%and 98.82%,respectively.On the NTU 120 dataset(Nanyang Technological University Red-Green-Blue-Depth dataset with 120 categories),RGB+Dthe accuracies on X-Sub and X-Set(the perspectives of the training set and the test set are different)are 91.97%and 93.45%,respectively.The model has achieved a balance in terms of accuracy,computation amount,and the number of parameters.展开更多
Objective:To explore the application effect of an information-based flipped classroom teaching model in nursing education of rheumatology and immunology,providing empirical evidence for optimizing specialized nursing ...Objective:To explore the application effect of an information-based flipped classroom teaching model in nursing education of rheumatology and immunology,providing empirical evidence for optimizing specialized nursing education models.Methods:Eighty nursing students from the 2021 grade of Xi’an Medical University were selected as research subjects and randomly divided into an experimental group(n=40)and a control group(n=40)using a random number table method.The control group received traditional lecture-based teaching,while the experimental group underwent information-based flipped classroom teaching.The theoretical knowledge scores and practical operation assessment scores of the two groups were compared after teaching,and the teaching satisfaction of the experimental group students was evaluated through a questionnaire survey.Results:The theoretical knowledge score(85.60±5.20)and practical operation score(88.30±4.70)of the experimental group were significantly higher than those of the control group(76.20±6.50)and(75.80±5.90),respectively.The differences were statistically significant(p<0.05).The satisfaction scores of all dimensions in the experimental group were significantly higher than those in the control group,and the differences were statistically significant(p<0.05).Conclusion:The information-based flipped classroom teaching model can effectively improve the quality of nursing teaching in rheumatology and immunology,enhance students’knowledge mastery and practical abilities,and is worthy of promotion and application in clinical nursing teaching.展开更多
To explore the mechanism through which the classroom environment influences the innovative behavior of medical students,this research paper used a sample of 1,019 medical students from four medical colleges in Sichuan...To explore the mechanism through which the classroom environment influences the innovative behavior of medical students,this research paper used a sample of 1,019 medical students from four medical colleges in Sichuan province.The paper then constructed and tested an integrated model that employs classroom environment as the independent variable,innovative behavior as the dependent variable,creative self-efficacy as the mediator variable,and teachers’emotional support as the moderator variable.The results show that the classroom environment and its six dimensions are all significantly and positively correlated with innovative behavior and can also significantly and positively predict it;creative self-efficacy plays a partial mediating role in the relationship between the classroom environment and innovative behavior;the emotional support of teachers positively moderates the influence of three types of teacher-led environments-the teacher-student relationship,the teaching method,and equal attention-on medical students’innovative behavior.The research findings have deepened the understanding of the mechanism that underlies the innovative behavior of medical students and highlight the importance of psychological empowerment and contextualized support in the cultivation of innovative talents in medical education.展开更多
One of the core challenges faced by children with autism is repetitive motor behavior,which not only undermines their learning efficiency in the classroom but also makes it difficult for them to integrate smoothly int...One of the core challenges faced by children with autism is repetitive motor behavior,which not only undermines their learning efficiency in the classroom but also makes it difficult for them to integrate smoothly into inclusive educational settings,thereby hindering their social adaptation and overall development.This study adopted a single-case design focusing on a first-grade student with autism enrolled in a mainstream class at Xiangxue Primary School,Huangpu District,Guangzhou.Functional behavior assessment was conducted to systematically observe and analyze the student’s repetitive motor behaviors in class,on the basis of which an intervention plan was developed and implemented.The intervention incorporated antecedent control,replacement behavior training,and consequence management,supplemented by collaboration between school and family.The findings indicated that the frequency of repetitive motor behaviors decreased significantly after the intervention,while the student demonstrated improved classroom participation and peer interaction;these positive effects were maintained during the follow-up phase.The study suggests that in inclusive education settings,interventions guided by functional behavior assessment can effectively reduce problem behaviors in children with autism and enhance their classroom engagement and learning adaptability.展开更多
This study employed a mixed methods approach to investigate the impact of learner and teacher variables on foreign language enjoyment(FLE)and foreign language anxiety(FLA)among 250 high school English learners in the ...This study employed a mixed methods approach to investigate the impact of learner and teacher variables on foreign language enjoyment(FLE)and foreign language anxiety(FLA)among 250 high school English learners in the Xinjiang Uygur Autonomous Region,China,a group that has so far been under-researched.Quantitative analysis results indicated a prevalence of FLE over FLA,demonstrating a significant negative correlation between these emotional dimensions.However,complete overlap between these emotions was not observed.Utilizing multiple regression models in R revealed main effects of Grade,Self-reported English Proficiency,Learners’Attitudes towards Teachers,and Teachers’Frequency of English Use on FLE.Conversely,only self-reported English Proficiency exhibited a main effect on FLA.Unlike most prior studies,gender did not exhibit a main effect;however,it interacted with Grade in influencing FLE and interacted with Learners’Attitudes towards Teachers regarding FLA.Qualitative analysis of participant responses elucidated factors contributing to enjoyable experiences,encompassing authentic English usage,self-recognition,teacher recognition and peer recognition.Conversely,factors such as mistakes made in assignments and unpreparedness for lessons,triggered anxious experiences.The pedagogical implication underscores that the classroom emotional climate is co-shaped by both students and teachers,with teachers playing a pivotal role in fostering FLE.展开更多
Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their ...Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their practical classroom deployment.To address this issue of low accuracy,we propose an improved YOLOv11-based detector that integrates CARAFE upsampling,DySnakeConv,DyHead,and SMFA fusion modules.This new model for real-time classroom behavior detection captures fine-grained student behaviors with low latency.Additionally,we have developed a visualization system that presents data through intuitive dashboards.This system enables teachers to dynamically grasp classroom engagement by tracking student participation and involvement.The enhanced YOLOv11 model achieves an mAP@0.5 of 87.2%on the evaluated datasets,surpassing baseline models.This significance lies in two aspects.First,it provides a practical technical route for deployable live classroom behavior monitoring and engagement feedback systems.Second,by integrating this proposed system,educators could make data-informed and fine-grained teaching decisions,ultimately improving instructional quality and learning outcomes.展开更多
In the new educational and teaching environment,teaching work at each stage is continuously updated to ensure high-quality development.By applying new teaching ideas,educators can truly enter the growth space of stude...In the new educational and teaching environment,teaching work at each stage is continuously updated to ensure high-quality development.By applying new teaching ideas,educators can truly enter the growth space of students,enabling them to have in-depth experiences and insights,thereby continuously enhancing students’interest in learning.For junior high school physical education and health courses,during the actual reform process,it is more necessary to base on the methods mentioned in this article,deeply improve the curriculum system,connect with highquality development,promptly transform the roles of teachers and students,set clear teaching goals,and help students establish the awareness of lifelong physical education.This ensures the comprehensive physical and mental growth and progress of students and enhances the actual quality of classroom teaching.展开更多
Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for ...Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures,and inconsistent objects.To address this challenge,we proposed an effective,lightweight object detector method called the RFNet model(YOLO-FR).The YOLO-FR is a lightweight and effective model.Specifically,for efficient multi-scale feature extraction,effective feature pyramid shared convolutional(FPSC)was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone.Secondly,to address the problem of multi-scale variability in the scene,we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network(RGCSPELAN)to improve the network performance further and reduce the amount of computation and the number of parameters.In addition,by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset.Experimental results indicate that,compared to the baseline model,the RFNet model has increased mean accuracy precision(mAP@50)from 69.6%to 71.0%on the SCB dataset3 and from 91.8%to 93.1%on the STBD-08 dataset.The RFNet approach has effectiveness precision at 68.6%,surpassing the baseline method(YOLOv11)at 3.3%and archieve the minimal size(4.9 M)on the SCB dataset3.Finally,comparing it with other algorithms,it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.展开更多
基金supported by U.S.Office of Naval Research(ONR)Grant number G2A62826.
文摘Purpose:This research addresses the challenge of concept drift in AI-enabled software,particularly within autonomous vehicle systems where concept drift in object recognition(like pedestrian detection)can lead to misclassifications and safety risks.This study introduces a proactive framework to detect early signs of domain-specific concept drift by leveraging domain analysis and natural language processing techniques.This method is designed to help maintain the relevance of domain knowledge and prevent potential failures in AI systems due to evolving concept definitions.Design/methodology/approach:The proposed framework integrates natural language processing and image analysis to continuously update and monitor key domain concepts against evolving external data sources,such as social media and news.By identifying terms and features closely associated with core concepts,the system anticipates and flags significant changes.This was tested in the automotive domain on the pedestrian concept,where the framework was evaluated for its capacity to detect shifts in the recognition of pedestrians,particularly during events like Halloween and specific car accidents.Findings:The framework demonstrated an ability to detect shifts in the domain concept of pedestrians,as evidenced by contextual changes around major events.While it successfully identified pedestrian-related drift,the system’s accuracy varied when overlapping with larger social events.The results indicate the model’s potential to foresee relevant shifts before they impact autonomous systems,although further refinement is needed to handle high-impact concurrent events.Research limitations:This study focused on detecting concept drift in the pedestrian domain within autonomous vehicles,with results varying across domains.To assess generalizability,we tested the framework for airplane-related incidents and demonstrated adaptability.However,unpredictable events and data biases from social media and news may obscure domain-specific drifts.Further evaluation across diverse applications is needed to enhance robustness in evolving AI environments.Practical implications:The proactive detection of concept drift has significant implications for AI-driven domains,especially in safety-critical applications like autonomous driving.By identifying early signs of drift,this framework provides actionable insights for AI system updates,potentially reducing misclassification risks and enhancing public safety.Moreover,it enables timely interventions,reducing costly and labor-intensive retraining requirements by focusing only on the relevant aspects of evolving concepts.This method offers a streamlined approach for maintaining AI system performance in environments where domain knowledge rapidly changes.Originality/value:This study contributes a novel domain-agnostic framework that combines natural language processing with image analysis to predict concept drift early.This unique approach,which is focused on real-time data sources,offers an effective and scalable solution for addressing the evolving nature of domain-specific concepts in AI applications.
文摘In recent years,artificial intelligence(AI)has been increasingly integrated into educational settings worldwide.This study aims to explore the effectiveness of AI classroom teaching for Chinese undergraduate students,focusing on its influence on learning outcomes and student engagement.The research uses a quantitative approach,utilizing surveys and academic performance data to evaluate two main objectives:(1)the impact of AI teaching methods on academic performance compared to traditional instruction;(2)the level of student engagement and satisfaction with AI-based learning tools.The study sample includes undergraduate students from multiple universities in China,allowing for a diverse representation of various disciplines.Data will be collected through standardized tests,questionnaires,and academic records,ensuring the reliability and validity of the results.The findings will provide insights into the potential advantages and challenges of AI integration in higher education and inform future strategies for adopting AI in Chinese classrooms.By exploring both the academic and practical aspects of AI-driven education,this research aims to contribute valuable knowledge to the growing field of AI in education,particularly in the context of Chinese higher education.The results are expected to have implications for educators,policymakers,and AI developers interested in enhancing the effectiveness of educational technologies.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFE0118700)the National Natural Science Foundation of China(Grant No.62375101)the Fundamental Research Funds for the Central Universities(Grant No.YCJJ20230216).
文摘Spectroscopy,especially for plasma spectroscopy,provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability.Artificial intelligence(AI)has the tremendous potential to build a universal quantitative framework covering all branches of plasma spectroscopy based on its unmatched representation and generalization ability.Herein,we introduce an AI-based unified method called self-supervised image-spectrum twin information fusion detection(SISTIFD)to collect twin co-occurrence signals of the plasma and to intelligently predict the physical parameters for improving the performances of all plasma spectroscopic techniques.It can fuse the spectra and plasma images in synchronization,derive the plasma parameters(total number density,plasma temperature,electron density,and other implicit factors),and provide accurate results.The experimental data demonstrate their excellent utility and capacity,with a reduction of 98%in evaluation indices(root mean square error,relative standard deviation,etc.)and an analysis frequency of 143 Hz(much faster than the mainstream detection frame rate of 1 Hz).In addition,as a completely end-to-end and self-supervised framework,the SISTIFD enables automatic detection without manual preprocessing or intervention.With these advantages,it has remarkably enhanced various plasma spectroscopic techniques with state-of-the-art performance and unsealed their possibility in industry,especially in the regions that require both capability and efficiency.This scheme brings new inspiration to the whole field of plasma spectroscopy and enables in situ analysis with a real-world scenario of high throughput,cross-interference,various analyte complexity,and diverse applications.
文摘This paper explains how the optimized classrooms were selected and the results that were achieved by the optimizations carried out and finalized.The context of the research is the city of Concepción,in Chile.Virtual models of classrooms were evaluated using the Radiance software.We used a methodology that allowed us to determine the luminous conditions under different types of skies,seasons of the year and times of the day.The evaluation of the typologies was performed based on three defined criteria,in order to achieve the stated design objectives.We defined the optimal solutions for each orientation and,finally,we stated design recommendations for daylit classrooms to ensure the visual comfort of the students.These recommendations link all that found in the initial analysis with that found in the optimization stage.
文摘Surgical nursing education has long grappled with a tension between traditional lecture-based models and actual practice needs.Students have difficulty converting theory into functional nursing skills in the passive format of knowledge acquisition.As medical education concepts have been renewed,the goals of educating nursing talents with independent judgment and adaptability to clinical practice have evolved into a significant direction of teaching reform.To this end,the“flipped classroom”model that blends lecture with discussion,through the mechanism of“internalized absorption”and“discussion”in the classroom,represents both a new teaching model that meets the need for integration of surgical nursing knowledge and an approach to skill application.This model reconstructs the teaching and learning process by providing a bridge for students to transfer from understanding knowledge to clinical thinking,thus providing a path forward for the dilemma of the separation of theory and practice in surgical nursing education.
文摘With the evolution of society,education reform continues to progress,and increasing attention is being paid to the learning outcomes and overall academic quality of junior high school students.As a core subject at this stage,the effectiveness of Chinese language teaching directly influences students’overall academic performance.Therefore,enhancing the effectiveness of junior high school Chinese classroom teaching has become a key focus for educators.Against this backdrop,this paper explores the significance of building an effective classroom environment in junior high school Chinese teaching and provides an in-depth analysis of strategies to improve teaching effectiveness.The goal is to assist teachers in creating efficient classrooms,fostering students’interest in learning,enhancing their overall competence in Chinese,and ultimately improving teaching quality to contribute to the overall advancement of Chinese language education.
文摘The widespread adoption of the internet has provided new platforms and possibilities for Chinese language instruction.Students can utilize online resources or mobile devices for learning outside the classroom,while teachers can shift the“intensive instruction”component of comprehensive Chinese courses to extracurricular settings.This approach enables increased practice time during class sessions,truly placing the learner at the center of the educational process.The flipped classroom model aligns with this philosophy and complements the disciplinary characteristics of comprehensive Chinese courses.In practice,implementing the O-PIRTAS universal flipped classroom model revealed its effectiveness in enhancing oral proficiency and overall competency.However,it is essential to concurrently address students’writing skills and cultivate their awareness of the flipped classroom approach.
基金Jiangsu Higher Education Teaching Reform Research Project(Project No.:2023JSJG766)Nanjing University of Science and Technology Undergraduate Teaching Reform and Construction Project(Project No.:2025BKJGO99)。
文摘In recent years,with the continuous development of educational digitalization,the integration model of SPOC(Small Private Online Course)and flipped classroom can integrate online teaching resources and offline teaching resources,overcome the shortcomings of traditional teaching models,and enhance students’enthusiasm for learning and autonomous learning ability.This model has become a popular direction in the reform of teaching models.Meanwhile,the advantages of the Zhihuishu knowledge graph platform,such as knowledge coherence and complete teaching modules,are highly beneficial for the promotion of the flipped classroom teaching model based on SPOC.This research will adopt methods such as literature review,investigation and research,questionnaire survey,and in-depth interview to analyze the application effect and optimization path of the flipped classroom teaching model based on SPOC on the Zhihuishu platform in college English teaching.The theoretical basis,practical effect and existing problems of the model were specifically analyzed,and corresponding optimization suggestions were put forward.
基金Key Research and Development Program of Shaanxi Province,“Exploring the Changes and Mechanisms of Cerebral Microcirculation in Patients with Cerebral Small Vessel Disease and Mild Cognitive Impairment Based on OCTA Assessment”(Project No.:2023-YBSF-569)Shaanxi Provincial People’s Hospital Science and Technology Development Incubation Fund,“Early Identification of Cognitive Impairment in Cerebral Small Vessel Disease Based on Eye Tracking Technology”(Project No.:2023YJY-72)Key Research and Development Program of Shaanxi Province,“Study on the Correlation between Deep Medullary Veins and Cognitive Dysfunction in Cerebral Small Vessel Disease”(Project No.:2023-YBSF-033)。
文摘Objective:To explore the application effect of group cooperative learning combined with flipped classroom in standardized training of emergency surgery resident physicians.Methods:95 resident physicians undergoing emergency surgery standardized training in our hospital were randomly divided into an experimental group(46,group cooperative learning+flipped classroom)and a control group(49,traditional teaching).The training period was 2 months.Results:The graduation assessment scores of the experimental group were higher than those of the control group(P<0.05).The improvement in critical thinking ability and self-learning ability was better than that of the control group(P<0.05).The response rate of“yes”to all items of course teaching satisfaction was significantly higher than that of the control group(P<0.05).The satisfaction scores for training teachers were higher than those of the control group(P<0.05).Conclusion:The teaching model of group cooperative learning combined with flipped classroom,through reconstructing the learning process and activating interactive participation,can significantly improve the clinical operation and theoretical foundation of emergency surgery standardized training students.It also cultivates core professional qualities such as critical thinking,self-learning,and team collaboration,providing a feasible paradigm for integrated medical education in this discipline.
文摘This paper focuses on the research on the teaching efficiency of ideological and political classrooms under the background of blended teaching.It analyzes the connotation and characteristics of blended teaching,explores the impact of blended teaching on the teaching efficiency of ideological and political classrooms from multiple aspects,and conducts empirical research through case analysis and data collection.The results show that blended teaching can effectively improve the teaching efficiency of ideological and political classrooms,enhance students’learning enthusiasm and participation,and promote the improvement of students’ideological and political qualities.Finally,corresponding suggestions and countermeasures are put forward to provide a reference for the improvement of the teaching quality of ideological and political courses.
基金supported by the National Natural Science Foundation of China under Grant 62107034the Major Science and Technology Project of Yunnan Province(202402AD080002)Yunnan International Joint R&D Center of China-Laos-Thailand Educational Digitalization(202203AP140006).
文摘The task of student action recognition in the classroom is to precisely capture and analyze the actions of students in classroom videos,providing a foundation for realizing intelligent and accurate teaching.However,the complex nature of the classroom environment has added challenges and difficulties in the process of student action recognition.In this research article,with regard to the circumstances where students are prone to be occluded and classroom computing resources are restricted in real classroom scenarios,a lightweight multi-modal fusion action recognition approach is put forward.This proposed method is capable of enhancing the accuracy of student action recognition while concurrently diminishing the number of parameters of the model and the Computation Amount,thereby achieving a more efficient and accurate recognition performance.In the feature extraction stage,this method fuses the keypoint heatmap with the RGB(Red-Green-Blue color model)image.In order to fully utilize the unique information of different modalities for feature complementarity,a Feature Fusion Module(FFE)is introduced.The FFE encodes and fuses the unique features of the two modalities during the feature extraction process.This fusion strategy not only achieves fusion and complementarity between modalities,but also improves the overall model performance.Furthermore,to reduce the computational load and parameter scale of the model,we use keypoint information to crop RGB images.At the same time,the first three networks of the lightweight feature extraction network X3D are used to extract dual-branch features.These methods significantly reduce the computational load and parameter scale.The number of parameters of the model is 1.40 million,and the computation amount is 5.04 billion floating-point operations per second(GFLOPs),achieving an efficient lightweight design.In the Student Classroom Action Dataset(SCAD),the accuracy of the model is 88.36%.In NTU 60(Nanyang Technological University Red-Green-Blue-Depth RGB+Ddataset with 60 categories),the accuracies on X-Sub(The people in the training set are different from those in the test set)and X-View(The perspectives of the training set and the test set are different)are 95.76%and 98.82%,respectively.On the NTU 120 dataset(Nanyang Technological University Red-Green-Blue-Depth dataset with 120 categories),RGB+Dthe accuracies on X-Sub and X-Set(the perspectives of the training set and the test set are different)are 91.97%and 93.45%,respectively.The model has achieved a balance in terms of accuracy,computation amount,and the number of parameters.
文摘Objective:To explore the application effect of an information-based flipped classroom teaching model in nursing education of rheumatology and immunology,providing empirical evidence for optimizing specialized nursing education models.Methods:Eighty nursing students from the 2021 grade of Xi’an Medical University were selected as research subjects and randomly divided into an experimental group(n=40)and a control group(n=40)using a random number table method.The control group received traditional lecture-based teaching,while the experimental group underwent information-based flipped classroom teaching.The theoretical knowledge scores and practical operation assessment scores of the two groups were compared after teaching,and the teaching satisfaction of the experimental group students was evaluated through a questionnaire survey.Results:The theoretical knowledge score(85.60±5.20)and practical operation score(88.30±4.70)of the experimental group were significantly higher than those of the control group(76.20±6.50)and(75.80±5.90),respectively.The differences were statistically significant(p<0.05).The satisfaction scores of all dimensions in the experimental group were significantly higher than those in the control group,and the differences were statistically significant(p<0.05).Conclusion:The information-based flipped classroom teaching model can effectively improve the quality of nursing teaching in rheumatology and immunology,enhance students’knowledge mastery and practical abilities,and is worthy of promotion and application in clinical nursing teaching.
基金funded by the Research Project of Primary Health Development Research Center of Sichuan Province:Study on the Path of Cultivating the Sense of Responsibility among Medical Students under the Epidemic Context-Taking Medical College Students in Sichuan Province as an Example(Project No.:SWFZ21-Y-45)the Project of the Center for Teacher Education Research at Sichuan Province of the Sichuan Provincial Key Research Base of Philosophy and Social Sciences:Research on the Approaches to Enhancing the Affinity of Ideological and Political Course Teachers in the New Era(Project No.:TER2021-22).
文摘To explore the mechanism through which the classroom environment influences the innovative behavior of medical students,this research paper used a sample of 1,019 medical students from four medical colleges in Sichuan province.The paper then constructed and tested an integrated model that employs classroom environment as the independent variable,innovative behavior as the dependent variable,creative self-efficacy as the mediator variable,and teachers’emotional support as the moderator variable.The results show that the classroom environment and its six dimensions are all significantly and positively correlated with innovative behavior and can also significantly and positively predict it;creative self-efficacy plays a partial mediating role in the relationship between the classroom environment and innovative behavior;the emotional support of teachers positively moderates the influence of three types of teacher-led environments-the teacher-student relationship,the teaching method,and equal attention-on medical students’innovative behavior.The research findings have deepened the understanding of the mechanism that underlies the innovative behavior of medical students and highlight the importance of psychological empowerment and contextualized support in the cultivation of innovative talents in medical education.
文摘One of the core challenges faced by children with autism is repetitive motor behavior,which not only undermines their learning efficiency in the classroom but also makes it difficult for them to integrate smoothly into inclusive educational settings,thereby hindering their social adaptation and overall development.This study adopted a single-case design focusing on a first-grade student with autism enrolled in a mainstream class at Xiangxue Primary School,Huangpu District,Guangzhou.Functional behavior assessment was conducted to systematically observe and analyze the student’s repetitive motor behaviors in class,on the basis of which an intervention plan was developed and implemented.The intervention incorporated antecedent control,replacement behavior training,and consequence management,supplemented by collaboration between school and family.The findings indicated that the frequency of repetitive motor behaviors decreased significantly after the intervention,while the student demonstrated improved classroom participation and peer interaction;these positive effects were maintained during the follow-up phase.The study suggests that in inclusive education settings,interventions guided by functional behavior assessment can effectively reduce problem behaviors in children with autism and enhance their classroom engagement and learning adaptability.
文摘This study employed a mixed methods approach to investigate the impact of learner and teacher variables on foreign language enjoyment(FLE)and foreign language anxiety(FLA)among 250 high school English learners in the Xinjiang Uygur Autonomous Region,China,a group that has so far been under-researched.Quantitative analysis results indicated a prevalence of FLE over FLA,demonstrating a significant negative correlation between these emotional dimensions.However,complete overlap between these emotions was not observed.Utilizing multiple regression models in R revealed main effects of Grade,Self-reported English Proficiency,Learners’Attitudes towards Teachers,and Teachers’Frequency of English Use on FLE.Conversely,only self-reported English Proficiency exhibited a main effect on FLA.Unlike most prior studies,gender did not exhibit a main effect;however,it interacted with Grade in influencing FLE and interacted with Learners’Attitudes towards Teachers regarding FLA.Qualitative analysis of participant responses elucidated factors contributing to enjoyable experiences,encompassing authentic English usage,self-recognition,teacher recognition and peer recognition.Conversely,factors such as mistakes made in assignments and unpreparedness for lessons,triggered anxious experiences.The pedagogical implication underscores that the classroom emotional climate is co-shaped by both students and teachers,with teachers playing a pivotal role in fostering FLE.
文摘Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their practical classroom deployment.To address this issue of low accuracy,we propose an improved YOLOv11-based detector that integrates CARAFE upsampling,DySnakeConv,DyHead,and SMFA fusion modules.This new model for real-time classroom behavior detection captures fine-grained student behaviors with low latency.Additionally,we have developed a visualization system that presents data through intuitive dashboards.This system enables teachers to dynamically grasp classroom engagement by tracking student participation and involvement.The enhanced YOLOv11 model achieves an mAP@0.5 of 87.2%on the evaluated datasets,surpassing baseline models.This significance lies in two aspects.First,it provides a practical technical route for deployable live classroom behavior monitoring and engagement feedback systems.Second,by integrating this proposed system,educators could make data-informed and fine-grained teaching decisions,ultimately improving instructional quality and learning outcomes.
文摘In the new educational and teaching environment,teaching work at each stage is continuously updated to ensure high-quality development.By applying new teaching ideas,educators can truly enter the growth space of students,enabling them to have in-depth experiences and insights,thereby continuously enhancing students’interest in learning.For junior high school physical education and health courses,during the actual reform process,it is more necessary to base on the methods mentioned in this article,deeply improve the curriculum system,connect with highquality development,promptly transform the roles of teachers and students,set clear teaching goals,and help students establish the awareness of lifelong physical education.This ensures the comprehensive physical and mental growth and progress of students and enhances the actual quality of classroom teaching.
基金suported by the Fundamental Research Grant Scheme(FRGS)of Universiti Sains Malaysia,Research Number:FRGS/1/2024/ICT02/USM/02/1.
文摘Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures,and inconsistent objects.To address this challenge,we proposed an effective,lightweight object detector method called the RFNet model(YOLO-FR).The YOLO-FR is a lightweight and effective model.Specifically,for efficient multi-scale feature extraction,effective feature pyramid shared convolutional(FPSC)was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone.Secondly,to address the problem of multi-scale variability in the scene,we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network(RGCSPELAN)to improve the network performance further and reduce the amount of computation and the number of parameters.In addition,by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset.Experimental results indicate that,compared to the baseline model,the RFNet model has increased mean accuracy precision(mAP@50)from 69.6%to 71.0%on the SCB dataset3 and from 91.8%to 93.1%on the STBD-08 dataset.The RFNet approach has effectiveness precision at 68.6%,surpassing the baseline method(YOLOv11)at 3.3%and archieve the minimal size(4.9 M)on the SCB dataset3.Finally,comparing it with other algorithms,it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.
