This study introduces an advanced recommender system for technology enhanced learning(TEL)that synergizes neural collaborative filtering,sentiment analysis,and an adaptive learning rate to address the limitations of t...This study introduces an advanced recommender system for technology enhanced learning(TEL)that synergizes neural collaborative filtering,sentiment analysis,and an adaptive learning rate to address the limitations of traditional TEL systems.Recognizing the critical gap in existing approaches—primarily their neglect of user emotional feedback and static learning paths—our model innovatively incorporates sentiment analysis to capture and respond to nuanced emotional feedback from users.Utilizing bidirectional encoder representations from Transformers for sentiment analysis,our system not only understands but also respects user privacy by processing feedback without revealing sensitive information.The adaptive learning rate,inspired by AdaGrad,allows our model to adjust its learning trajectory based on the sentiment scores associated with user feedback,ensuring a dynamic response to both positive and negative sentiments.This dual approach enhances the system’s adapt-ability to changing user preferences and improves its contentment understanding.Our methodology involves a comprehensive analysis of both the content of learning materials and the behaviors and preferences of learners,facilitating a more personalized learning experience.By dynamically adjusting recommendations based on real-time user data and behavioral analysis,our system leverages the collective insights of similar users and rele-vant content.We validated our approach against three datasets-MovieLens,Amazon,and a proprietary TEL dataset—and saw significant improvements in recommendation precision,F-score,and mean absolute error.The results indicate the potential of integrating sentiment analysis and adaptive learning rates into TEL recommender systems,marking a step forward in developing more responsive and user-centric educational technologies.This study paves the way for future advancements in TEL systems,emphasizing the importance of emotional intelli-gence and adaptability in enhancing the learning experience.展开更多
The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Base...The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.展开更多
Background and Objective:Social media(SoMe)has emerged as a tool in health professions education(HPE),particularly amidst the challenges posed by the coronavirus disease 2019(COVID-19)pandemic.Despite the academia’s ...Background and Objective:Social media(SoMe)has emerged as a tool in health professions education(HPE),particularly amidst the challenges posed by the coronavirus disease 2019(COVID-19)pandemic.Despite the academia’s initial skepticism SoMe has been gaining traction in supporting learning communities,and offering opportunities for innovation in HPE.Our study aims to explore the integration of SoMe in HPE.Four key components were outlined as necessary for a successful integration,and include designing learning experiences,defining educator roles,selecting appropriate platforms,and establishing educational objectives.Methods:This article stemmed from the online Teaching Skills Series module on SoMe in education from the Ophthalmology Foundation,and drew upon evidence supporting learning theories relevant to SoMe integration and models of education.Additionally,we conducted a literature review considering Englishlanguage articles on the application of SoMe in ophthalmology from PubMed over the past decade.Key Content and Findings:Early adopters of SoMe platforms in HPE have leveraged these tools to enhance learning experiences through interaction,dialogue,content sharing,and active learning strategies.By integrating SoMe into educational programs,both online and in-person,educators can overcome time and geographical constraints,fostering more diverse and inclusive learning communities.Careful consideration is,however,necessary to address potential limitations within HPE.Conclusions:This article lays groundwork for expanding SoMe integration in HPE design,emphasizing the supportive scaffold of various learning theories,and the need of furthering robust research on examining its advantages over traditional educational formats.Our literature review underscores an ongoing multifaceted,random application of SoMe platforms in ophthalmology education.We advocate for an effective incorporation of SoMe in HPE education,with the need to comply with good educational practice.展开更多
Artificial intelligence (AI) has garnered significant interest within the educational domain over the past decade, promising to revolutionise teaching and learning. This paper provides a comprehensive overview of syst...Artificial intelligence (AI) has garnered significant interest within the educational domain over the past decade, promising to revolutionise teaching and learning. This paper provides a comprehensive overview of systematic reviews conducted from 2010 to 2023 on the implementation of AI in K-12 education. By synthesising findings from ten selected systematic reviews, this study explores the multifaceted opportunities and challenges posed by AI in education. The analysis reveals several key findings: AI’s potential to personalise learning, enhance student motivation, and improve teaching efficiency are highlighted as major strengths. However, the study also identifies critical concerns, including teacher resistance, high implementation costs, ethical considerations, and the need for extensive teacher training. These findings represent the most significant insights from the analysis, while additional findings further underscore the complexity and scope of AI integration in educational settings. The study employs a SWOT analysis to summarise these insights, identifying key areas for future research and policy development. This review aims to guide educators, policymakers, and researchers in effectively leveraging AI to enhance educational outcomes while addressing its inherent challenges.展开更多
A new algorithm called spatially aware routing algorithm with enhanced learning (SAREL) is proposed to guarantee the rationality of route selecting in inter-vehicle communication scenario. Firstly, the traffic model i...A new algorithm called spatially aware routing algorithm with enhanced learning (SAREL) is proposed to guarantee the rationality of route selecting in inter-vehicle communication scenario. Firstly, the traffic model is discussed and set up by using Poisson distribution. Then we analyze the process of traffic evaluation with enhanced learning, and exploit movement estimation to assist state memorization. The improvement of algorithm is provided at last compared with our previous work. Simulation results show that SAREL algorithm could achieve better performance in packet delivery ratio, especially when network connection ratio is average. Key words mobile ad hoc network - spatially aware routing - enhanced learning CLC number TP 316 Foundation item: Supported by Open Laboratory Foundation by China Ministry of Education (TKLJ9903), Project CarTALK 2000 by the European Commission (IST-2000-28185) and Project FleetNet-Internet on the Road by the German Ministry of Education and Research (01AK025)Biography: HAN Lu (1974-), male, Ph. D candidate, research direction; distributed artificial intelligence.展开更多
In recent years,the enabling role of technology has been widely recognized in second language teaching and learning.Using a co-word analysis approach,this study aims to present the state-ofthe-art current literature i...In recent years,the enabling role of technology has been widely recognized in second language teaching and learning.Using a co-word analysis approach,this study aims to present the state-ofthe-art current literature in technology enhanced language learning.The analysis results show that most popular research themes from 2010 to 2020 are CMC,L2 writing,teacher education,L2 motivation,students’attitudes,SLA,L2 vocabulary learning,L2 reading,EFL,and L2 learners.Compared with these key themes,publications on technology enhanced teaching Chinese as a foreign language focus more on the investigation of the effectiveness of the use of latest technologies such as smart phones,virtual reality,intelligent tools,and gamification.Based on the findings of these investigations,the latest developments in these areas are synthesized and presented accordingly.In addition,newly-emerged key themes about the co-word analysis are also reported and their contributions to future investigations is briefly discussed.The implications for future research in technology enhanced teaching Chinese as a foreign language is also discussed.展开更多
The principal objectives of IESO,the flag-ship activity of the International Geoscience Education Organisation(IGEO;www.geoscied.org),are to raise higher secondary school students’interest in earth science,to enhance...The principal objectives of IESO,the flag-ship activity of the International Geoscience Education Organisation(IGEO;www.geoscied.org),are to raise higher secondary school students’interest in earth science,to enhance earth science learning of students,to improve teaching of earth science in schools,to forge friendship among young learners from different countries and to promote international cooperation in exchanging ideas and materials about earth science and earth science education.展开更多
A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporat...A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporated into the convolutive nonnegative matrix fac- torization framework. The update rules for the time-varying gains and speech dictionary are derived by precomputing the noise dictionary. The magnitude spectra of speech are estimated using convolution operation between the learned speech dictionary and the time-varying gains. Finally, noise is removed via binary time-frequency masking. The experimental results indi- cate that the proposed scheme gives better enhancement results in terms of quality measures of speech. Moreover, the proposed algorithm outperforms the multiband spectra subtraction and the non-negative sparse coding based noise reduction algorithm in nonstationary noise conditions.展开更多
文摘This study introduces an advanced recommender system for technology enhanced learning(TEL)that synergizes neural collaborative filtering,sentiment analysis,and an adaptive learning rate to address the limitations of traditional TEL systems.Recognizing the critical gap in existing approaches—primarily their neglect of user emotional feedback and static learning paths—our model innovatively incorporates sentiment analysis to capture and respond to nuanced emotional feedback from users.Utilizing bidirectional encoder representations from Transformers for sentiment analysis,our system not only understands but also respects user privacy by processing feedback without revealing sensitive information.The adaptive learning rate,inspired by AdaGrad,allows our model to adjust its learning trajectory based on the sentiment scores associated with user feedback,ensuring a dynamic response to both positive and negative sentiments.This dual approach enhances the system’s adapt-ability to changing user preferences and improves its contentment understanding.Our methodology involves a comprehensive analysis of both the content of learning materials and the behaviors and preferences of learners,facilitating a more personalized learning experience.By dynamically adjusting recommendations based on real-time user data and behavioral analysis,our system leverages the collective insights of similar users and rele-vant content.We validated our approach against three datasets-MovieLens,Amazon,and a proprietary TEL dataset—and saw significant improvements in recommendation precision,F-score,and mean absolute error.The results indicate the potential of integrating sentiment analysis and adaptive learning rates into TEL recommender systems,marking a step forward in developing more responsive and user-centric educational technologies.This study paves the way for future advancements in TEL systems,emphasizing the importance of emotional intelli-gence and adaptability in enhancing the learning experience.
文摘The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.
文摘Background and Objective:Social media(SoMe)has emerged as a tool in health professions education(HPE),particularly amidst the challenges posed by the coronavirus disease 2019(COVID-19)pandemic.Despite the academia’s initial skepticism SoMe has been gaining traction in supporting learning communities,and offering opportunities for innovation in HPE.Our study aims to explore the integration of SoMe in HPE.Four key components were outlined as necessary for a successful integration,and include designing learning experiences,defining educator roles,selecting appropriate platforms,and establishing educational objectives.Methods:This article stemmed from the online Teaching Skills Series module on SoMe in education from the Ophthalmology Foundation,and drew upon evidence supporting learning theories relevant to SoMe integration and models of education.Additionally,we conducted a literature review considering Englishlanguage articles on the application of SoMe in ophthalmology from PubMed over the past decade.Key Content and Findings:Early adopters of SoMe platforms in HPE have leveraged these tools to enhance learning experiences through interaction,dialogue,content sharing,and active learning strategies.By integrating SoMe into educational programs,both online and in-person,educators can overcome time and geographical constraints,fostering more diverse and inclusive learning communities.Careful consideration is,however,necessary to address potential limitations within HPE.Conclusions:This article lays groundwork for expanding SoMe integration in HPE design,emphasizing the supportive scaffold of various learning theories,and the need of furthering robust research on examining its advantages over traditional educational formats.Our literature review underscores an ongoing multifaceted,random application of SoMe platforms in ophthalmology education.We advocate for an effective incorporation of SoMe in HPE education,with the need to comply with good educational practice.
文摘Artificial intelligence (AI) has garnered significant interest within the educational domain over the past decade, promising to revolutionise teaching and learning. This paper provides a comprehensive overview of systematic reviews conducted from 2010 to 2023 on the implementation of AI in K-12 education. By synthesising findings from ten selected systematic reviews, this study explores the multifaceted opportunities and challenges posed by AI in education. The analysis reveals several key findings: AI’s potential to personalise learning, enhance student motivation, and improve teaching efficiency are highlighted as major strengths. However, the study also identifies critical concerns, including teacher resistance, high implementation costs, ethical considerations, and the need for extensive teacher training. These findings represent the most significant insights from the analysis, while additional findings further underscore the complexity and scope of AI integration in educational settings. The study employs a SWOT analysis to summarise these insights, identifying key areas for future research and policy development. This review aims to guide educators, policymakers, and researchers in effectively leveraging AI to enhance educational outcomes while addressing its inherent challenges.
文摘A new algorithm called spatially aware routing algorithm with enhanced learning (SAREL) is proposed to guarantee the rationality of route selecting in inter-vehicle communication scenario. Firstly, the traffic model is discussed and set up by using Poisson distribution. Then we analyze the process of traffic evaluation with enhanced learning, and exploit movement estimation to assist state memorization. The improvement of algorithm is provided at last compared with our previous work. Simulation results show that SAREL algorithm could achieve better performance in packet delivery ratio, especially when network connection ratio is average. Key words mobile ad hoc network - spatially aware routing - enhanced learning CLC number TP 316 Foundation item: Supported by Open Laboratory Foundation by China Ministry of Education (TKLJ9903), Project CarTALK 2000 by the European Commission (IST-2000-28185) and Project FleetNet-Internet on the Road by the German Ministry of Education and Research (01AK025)Biography: HAN Lu (1974-), male, Ph. D candidate, research direction; distributed artificial intelligence.
基金The 2016 major project of one hundred key research bases of Humanities and Social Sciences of the Ministry of Education Fund 16JJD740004 China。
文摘In recent years,the enabling role of technology has been widely recognized in second language teaching and learning.Using a co-word analysis approach,this study aims to present the state-ofthe-art current literature in technology enhanced language learning.The analysis results show that most popular research themes from 2010 to 2020 are CMC,L2 writing,teacher education,L2 motivation,students’attitudes,SLA,L2 vocabulary learning,L2 reading,EFL,and L2 learners.Compared with these key themes,publications on technology enhanced teaching Chinese as a foreign language focus more on the investigation of the effectiveness of the use of latest technologies such as smart phones,virtual reality,intelligent tools,and gamification.Based on the findings of these investigations,the latest developments in these areas are synthesized and presented accordingly.In addition,newly-emerged key themes about the co-word analysis are also reported and their contributions to future investigations is briefly discussed.The implications for future research in technology enhanced teaching Chinese as a foreign language is also discussed.
文摘The principal objectives of IESO,the flag-ship activity of the International Geoscience Education Organisation(IGEO;www.geoscied.org),are to raise higher secondary school students’interest in earth science,to enhance earth science learning of students,to improve teaching of earth science in schools,to forge friendship among young learners from different countries and to promote international cooperation in exchanging ideas and materials about earth science and earth science education.
文摘A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporated into the convolutive nonnegative matrix fac- torization framework. The update rules for the time-varying gains and speech dictionary are derived by precomputing the noise dictionary. The magnitude spectra of speech are estimated using convolution operation between the learned speech dictionary and the time-varying gains. Finally, noise is removed via binary time-frequency masking. The experimental results indi- cate that the proposed scheme gives better enhancement results in terms of quality measures of speech. Moreover, the proposed algorithm outperforms the multiband spectra subtraction and the non-negative sparse coding based noise reduction algorithm in nonstationary noise conditions.
