The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack sys...The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation.This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes(DECOC),Long Short-Term Memory(LSTM)networks,and Multi-Layer Deep Neural Networks(ML-DNN)to identify optimal emoji placements within computer science course materials.The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content.A meticulously annotated dataset,comprising diverse topics in computer science,was developed to train and validate the model,ensuring its applicability across a wide range of educational contexts.Comprehensive validation demonstrated the system’s superior performance,achieving an accuracy of 92.4%,precision of 90.7%,recall of 89.3%,and an F1-score of 90.0%.Comparative analysis with baselinemodels and relatedworks confirms themodel’s ability tooutperformexisting approaches inbalancing accuracy,relevance,and contextual appropriateness.Beyond its technical advancements,this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted(AI-assisted)tool that facilitates personalized content adaptation based on student sentiment and engagement patterns.By automating the identification of appropriate emoji placements,teachers can enhance digital course materials with minimal effort,improving the clarity of complex concepts and fostering an emotionally supportive learning environment.This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support.Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials,offering an innovative tool for fostering student engagement and enhancing learning outcomes.The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources,emphasizing scalability and adaptability for broader applications in e-learning.展开更多
COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of en...COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.展开更多
This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combin...This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combining quantitative surveys and qualitative interviews to understand teachers’perceptions and attitudes,and the factors influencing their adoption of LMS data analysis tools.The findings reveal that perceived usefulness,perceived ease of use,technical literacy,organizational support,and data privacy concerns significantly impact teachers’willingness to use these tools.Based on these insights,the study offers practical recommendations for educational institutions to enhance the effective adoption of LMS data analysis tools in English language teaching.展开更多
Zambia like any other country in most African regions is still grappling with the dynamics of harnessing technology for the betterment of Higher Education. The onset of the Covid 19 pandemic brought a test for the pre...Zambia like any other country in most African regions is still grappling with the dynamics of harnessing technology for the betterment of Higher Education. The onset of the Covid 19 pandemic brought a test for the preparedness of the Zambian Higher Education Institutions (HEIs) in harnessing technology for pedagogical activities. As countries worldwide switched to electronic learning during the pandemic, the same could not be said for Zambian HEIs. Zambian HEIs struggled to conduct pedagogical activities on learning management platforms. This study investigated the factors affecting the implementation and assessment of learning Management systems in Zambia’s HEIs. With its focus on assessing: 1) system features, 2) compliance with regulatory standards, 3) quality of service and 4) technology acceptance as the four key assessment areas of an LMS, this article proposed a model for assessing learning management systems in Zambian HEIs. To test the proposed model, a software tool was also developed.展开更多
A learning management system(LMS)is a software or web based application,commonly utilized for planning,designing,and assessing a particular learning procedure.Generally,the LMS offers a method of creating and deliveri...A learning management system(LMS)is a software or web based application,commonly utilized for planning,designing,and assessing a particular learning procedure.Generally,the LMS offers a method of creating and delivering content to the instructor,monitoring students’involvement,and validating their outcomes.Since mental health issues become common among studies in higher education globally,it is needed to properly determine it to improve mental stabi-lity.This article develops a new seven spot lady bird feature selection with opti-mal sparse autoencoder(SSLBFS-OSAE)model to assess students’mental health on LMS.The major aim of the SSLBFS-OSAE model is to determine the proper health status of the students with respect to depression,anxiety,and stress(DAS).The SSLBFS-OSAE model involves a new SSLBFS model to elect a useful set of features.In addition,OSAE model is applied for the classification of mental health conditions and the performance can be improved by the use of cuckoo search optimization(CSO)based parameter tuning process.The design of CSO algorithm for optimally tuning the SAE parameters results in enhanced classifica-tion outcomes.For examining the improved classifier results of the SSLBFS-OSAE model,a comprehensive results analysis is done and the obtained values highlighted the supremacy of the SSLBFS model over its recent methods interms of different measures.展开更多
The Learning management system(LMS)is now being used for uploading educational content in both distance and blended setups.LMS platform has two types of users:the educators who upload the content,and the students who ...The Learning management system(LMS)is now being used for uploading educational content in both distance and blended setups.LMS platform has two types of users:the educators who upload the content,and the students who have to access the content.The students,usually rely on text notes or books and video tutorials while their exams are conducted with formal methods.Formal assessments and examination criteria are ineffective with restricted learning space which makes the student tend only to read the educational contents and videos instead of interactive mode.The aim is to design an interactive LMS and examination video-based interface to cater the issues of educators and students.It is designed according to Human-computer interaction(HCI)principles to make the interactive User interface(UI)through User experience(UX).The interactive lectures in the form of annotated videos increase user engagement and improve the self-study context of users involved in LMS.The interface design defines how the design will interact with users and how the interface exchanges information.The findings show that interactive videos for LMS allow the users to have a more personalized learning experience by engaging in the educational content.The result shows a highly personalized learning experience due to the interactive video and quiz within the video.展开更多
Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning...Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.展开更多
Learning analytics is an emerging technique of analysing student par-ticipation and engagement.The recent COVID-19 pandemic has significantly increased the role of learning management systems(LMSs).LMSs previously only...Learning analytics is an emerging technique of analysing student par-ticipation and engagement.The recent COVID-19 pandemic has significantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to 2020.To date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and instructors.Consequently,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notfirst identified.Therefore,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an LMS.The required data was collected from an LMS that is currently utilised by a local Malaysian university.The model was validated by a panel of experts as well as discussions with students.It is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.展开更多
Learning analytics is a rapidly evolving research discipline that uses theinsights generated from data analysis to support learners as well as optimize boththe learning process and environment. This paper studied stud...Learning analytics is a rapidly evolving research discipline that uses theinsights generated from data analysis to support learners as well as optimize boththe learning process and environment. This paper studied students’ engagementlevel of the Learning Management System (LMS) via a learning analytics tool,student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review(SLR) was employed for the selection, sorting and exclusion of articles fromdiverse renowned sources. The findings show that most of the engagement inLMS are driven by educators. Additionally, we have discussed the factors inLMS, causes of low engagement and ways of increasing engagement factorsvia the Learning Analytics approach. Nevertheless, apart from recognizing theLearning Analytics approach as being a successful method and technique for analyzing the LMS data, this research further highlighted the possibility of mergingthe learning analytics technique with the LMS engagement in every institution asbeing a direction for future research.展开更多
Great transformations in technology have affected the education sector in the recent years.The mode of teaching,learning and assessment have been transformed from brick and mortar enabling Several Universities to util...Great transformations in technology have affected the education sector in the recent years.The mode of teaching,learning and assessment have been transformed from brick and mortar enabling Several Universities to utilize the Learning Management System to reach out to distance learners.Whereas student centered distance learning requires that students collaborate on their learning tasks and obtain timely feedback;this is not the practice with lecturers at universities.Their practice of using LMS does not meet students’requirements in distance learning because it is not adquently authentic.While distance learners are on the LMS,they should be able to engage with content that stimulates authentic context,authentic activity,expert performance and multiple perspectives.However,the content we see on the LMS that these distance students engage with does not portray authentic context,authentic activity,expert performance and multiple perspectives.This research seeks to answer the following question:How is content designed on MUELE platform?An action research case study was adopted where six course units were observed online and eight participants interviewed,guided by Herrington and Oliver(2000)framework for authentic learning.Data was analyzed and findings revealed that most content was not designed in line with authentic principles especially in theoretical courses which contradicts the framework for authentic learning.In conclusion,authentic learning principles provide effective guidelines for implementing teaching and assessments online.Therefore,the study recommends that;the design of content in all courses should follow the authentic learning principles,such as authentic context,authentic activity,multiple perspectives and expert performances.Lastly,teacher motivation and training on how to assess students in an online environment needs to be encouraged.展开更多
The dramatic improvement of information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and ...The dramatic improvement of information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. This paper discusses how Artificial Intelligence and Machine Learning techniques are adopted to fulfill users’ needs in a social learning management system named “CourseNetworking”. The paper explains how machine learning contributed to developing an intelligent agent called “Rumi” as a personal assistant in CourseNetworking platform to add personalization, gamification, and more dynamics to the system. This paper aims to introduce machine learning to traditional learning platforms and guide the developers working in LMS field to benefit from advanced technologies in learning platforms by offering customized services.展开更多
Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast ele...Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and complex.This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)network.Leveraging a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear relationships.The bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal direction.This design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and robustness.Compared to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these benchmarks.These results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities.By integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.展开更多
An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontol...An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontology and user ontology, and evaluated from its function and implementation; then the corresponding class diagram, resource description framework （RDF） schema and extensible markup language （XML） schema are given. Secondly, the metadata for online learning resource repository management is proposed based on the Dublin Core Metadata Initiative and the IEEE Learning Technologies Standards Committee Learning Object Metadata Working Group. Finally, the inference instance is shown, which proves the validity of ontology and metadata in online learning resource repository management.展开更多
In undergraduate fashion design education,the main focus is on clothing design,fashion brand garments,and design trends,especially on how to create and market these designs.Higher vocational education emphasizes devel...In undergraduate fashion design education,the main focus is on clothing design,fashion brand garments,and design trends,especially on how to create and market these designs.Higher vocational education emphasizes developing design skills and methods,transforming design processes,and managing clothing production.In China,the first undergraduate major in fashion design started in 1983 at Suzhou Silk Institute of Technology,which used to be linked to the Ministry of Textile Industry.The mode of teaching and learning delivery of fashion design courses just like other courses,started with face-to-face teaching in physical classroom and practical training classes.However,due to fast development in educational technology and movement restriction during COVID-19 pandemic,the fashion design education is adapting the innovative learning management system and revolutionized into online and hybrid delivery of teaching and learning content.In this review,fashion design courses in China education system will be elaborated and comparison of physical,online,and hybrid delivery of fashion design course will be discussed.展开更多
With the prosperity of the Intemet, e-learning has been greatly improved. By supporting multiple learners and multiple roles in a learning activity, the IMS Leaming Design （LD） specification provides a collaborative...With the prosperity of the Intemet, e-learning has been greatly improved. By supporting multiple learners and multiple roles in a learning activity, the IMS Leaming Design （LD） specification provides a collaborative scenario for participants. However, IMS LD provides insufficient support for interaction among learning activities and can not dynamically integrate learning resources to meet the continually changing service requirements. In this paper, a Business Process Execution Language （BPEL） enhanced requirement driven learning management architecture to address the issues of personalize adaptive learning was proposed. It models the learning activity by combining IMS LD with BPEL and matches optimal learning sequence based on Case-based reasoning （CBR） method. By providing expandable secure learning sequences flexibly, it satisfies the different actual demands for personalize learning展开更多
基金funded by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University,grant number[R-2025-1637].
文摘The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation.This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes(DECOC),Long Short-Term Memory(LSTM)networks,and Multi-Layer Deep Neural Networks(ML-DNN)to identify optimal emoji placements within computer science course materials.The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content.A meticulously annotated dataset,comprising diverse topics in computer science,was developed to train and validate the model,ensuring its applicability across a wide range of educational contexts.Comprehensive validation demonstrated the system’s superior performance,achieving an accuracy of 92.4%,precision of 90.7%,recall of 89.3%,and an F1-score of 90.0%.Comparative analysis with baselinemodels and relatedworks confirms themodel’s ability tooutperformexisting approaches inbalancing accuracy,relevance,and contextual appropriateness.Beyond its technical advancements,this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted(AI-assisted)tool that facilitates personalized content adaptation based on student sentiment and engagement patterns.By automating the identification of appropriate emoji placements,teachers can enhance digital course materials with minimal effort,improving the clarity of complex concepts and fostering an emotionally supportive learning environment.This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support.Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials,offering an innovative tool for fostering student engagement and enhancing learning outcomes.The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources,emphasizing scalability and adaptability for broader applications in e-learning.
文摘COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.
文摘This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combining quantitative surveys and qualitative interviews to understand teachers’perceptions and attitudes,and the factors influencing their adoption of LMS data analysis tools.The findings reveal that perceived usefulness,perceived ease of use,technical literacy,organizational support,and data privacy concerns significantly impact teachers’willingness to use these tools.Based on these insights,the study offers practical recommendations for educational institutions to enhance the effective adoption of LMS data analysis tools in English language teaching.
文摘Zambia like any other country in most African regions is still grappling with the dynamics of harnessing technology for the betterment of Higher Education. The onset of the Covid 19 pandemic brought a test for the preparedness of the Zambian Higher Education Institutions (HEIs) in harnessing technology for pedagogical activities. As countries worldwide switched to electronic learning during the pandemic, the same could not be said for Zambian HEIs. Zambian HEIs struggled to conduct pedagogical activities on learning management platforms. This study investigated the factors affecting the implementation and assessment of learning Management systems in Zambia’s HEIs. With its focus on assessing: 1) system features, 2) compliance with regulatory standards, 3) quality of service and 4) technology acceptance as the four key assessment areas of an LMS, this article proposed a model for assessing learning management systems in Zambian HEIs. To test the proposed model, a software tool was also developed.
基金supported by the Researchers Supporting Program(TUMA-Project-2021-31)supported by the Researchers Supporting Program(TUMA-Project-2021-27)Almaarefa University,Riyadh,Saudi Arabia.
文摘A learning management system(LMS)is a software or web based application,commonly utilized for planning,designing,and assessing a particular learning procedure.Generally,the LMS offers a method of creating and delivering content to the instructor,monitoring students’involvement,and validating their outcomes.Since mental health issues become common among studies in higher education globally,it is needed to properly determine it to improve mental stabi-lity.This article develops a new seven spot lady bird feature selection with opti-mal sparse autoencoder(SSLBFS-OSAE)model to assess students’mental health on LMS.The major aim of the SSLBFS-OSAE model is to determine the proper health status of the students with respect to depression,anxiety,and stress(DAS).The SSLBFS-OSAE model involves a new SSLBFS model to elect a useful set of features.In addition,OSAE model is applied for the classification of mental health conditions and the performance can be improved by the use of cuckoo search optimization(CSO)based parameter tuning process.The design of CSO algorithm for optimally tuning the SAE parameters results in enhanced classifica-tion outcomes.For examining the improved classifier results of the SSLBFS-OSAE model,a comprehensive results analysis is done and the obtained values highlighted the supremacy of the SSLBFS model over its recent methods interms of different measures.
文摘The Learning management system(LMS)is now being used for uploading educational content in both distance and blended setups.LMS platform has two types of users:the educators who upload the content,and the students who have to access the content.The students,usually rely on text notes or books and video tutorials while their exams are conducted with formal methods.Formal assessments and examination criteria are ineffective with restricted learning space which makes the student tend only to read the educational contents and videos instead of interactive mode.The aim is to design an interactive LMS and examination video-based interface to cater the issues of educators and students.It is designed according to Human-computer interaction(HCI)principles to make the interactive User interface(UI)through User experience(UX).The interactive lectures in the form of annotated videos increase user engagement and improve the self-study context of users involved in LMS.The interface design defines how the design will interact with users and how the interface exchanges information.The findings show that interactive videos for LMS allow the users to have a more personalized learning experience by engaging in the educational content.The result shows a highly personalized learning experience due to the interactive video and quiz within the video.
基金supported by the Researchers Supporting Program(TUMA-Project2021-27)Almaarefa University,RiyadhSaudi Arabia.Taif University Researchers Supporting Project number(TURSP-2020/161)Taif University,Taif,Saudi Arabia.
文摘Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.
文摘Learning analytics is an emerging technique of analysing student par-ticipation and engagement.The recent COVID-19 pandemic has significantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to 2020.To date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and instructors.Consequently,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notfirst identified.Therefore,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an LMS.The required data was collected from an LMS that is currently utilised by a local Malaysian university.The model was validated by a panel of experts as well as discussions with students.It is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.
基金supported by the University of Malaya,Bantuan Khas Penyelidikan under the research grant of BKS083-2017Fundamental Research Grant Scheme(FRGS)under Grant number FP112-2018A from the Ministry of Education Malaysia,Higher Education.
文摘Learning analytics is a rapidly evolving research discipline that uses theinsights generated from data analysis to support learners as well as optimize boththe learning process and environment. This paper studied students’ engagementlevel of the Learning Management System (LMS) via a learning analytics tool,student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review(SLR) was employed for the selection, sorting and exclusion of articles fromdiverse renowned sources. The findings show that most of the engagement inLMS are driven by educators. Additionally, we have discussed the factors inLMS, causes of low engagement and ways of increasing engagement factorsvia the Learning Analytics approach. Nevertheless, apart from recognizing theLearning Analytics approach as being a successful method and technique for analyzing the LMS data, this research further highlighted the possibility of mergingthe learning analytics technique with the LMS engagement in every institution asbeing a direction for future research.
文摘Great transformations in technology have affected the education sector in the recent years.The mode of teaching,learning and assessment have been transformed from brick and mortar enabling Several Universities to utilize the Learning Management System to reach out to distance learners.Whereas student centered distance learning requires that students collaborate on their learning tasks and obtain timely feedback;this is not the practice with lecturers at universities.Their practice of using LMS does not meet students’requirements in distance learning because it is not adquently authentic.While distance learners are on the LMS,they should be able to engage with content that stimulates authentic context,authentic activity,expert performance and multiple perspectives.However,the content we see on the LMS that these distance students engage with does not portray authentic context,authentic activity,expert performance and multiple perspectives.This research seeks to answer the following question:How is content designed on MUELE platform?An action research case study was adopted where six course units were observed online and eight participants interviewed,guided by Herrington and Oliver(2000)framework for authentic learning.Data was analyzed and findings revealed that most content was not designed in line with authentic principles especially in theoretical courses which contradicts the framework for authentic learning.In conclusion,authentic learning principles provide effective guidelines for implementing teaching and assessments online.Therefore,the study recommends that;the design of content in all courses should follow the authentic learning principles,such as authentic context,authentic activity,multiple perspectives and expert performances.Lastly,teacher motivation and training on how to assess students in an online environment needs to be encouraged.
文摘The dramatic improvement of information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. This paper discusses how Artificial Intelligence and Machine Learning techniques are adopted to fulfill users’ needs in a social learning management system named “CourseNetworking”. The paper explains how machine learning contributed to developing an intelligent agent called “Rumi” as a personal assistant in CourseNetworking platform to add personalization, gamification, and more dynamics to the system. This paper aims to introduce machine learning to traditional learning platforms and guide the developers working in LMS field to benefit from advanced technologies in learning platforms by offering customized services.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and complex.This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)network.Leveraging a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear relationships.The bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal direction.This design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and robustness.Compared to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these benchmarks.These results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities.By integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.
基金The Advanced University Action Plan of the Minis-try of Education of China (2004XD-03).
文摘An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontology and user ontology, and evaluated from its function and implementation; then the corresponding class diagram, resource description framework （RDF） schema and extensible markup language （XML） schema are given. Secondly, the metadata for online learning resource repository management is proposed based on the Dublin Core Metadata Initiative and the IEEE Learning Technologies Standards Committee Learning Object Metadata Working Group. Finally, the inference instance is shown, which proves the validity of ontology and metadata in online learning resource repository management.
文摘In undergraduate fashion design education,the main focus is on clothing design,fashion brand garments,and design trends,especially on how to create and market these designs.Higher vocational education emphasizes developing design skills and methods,transforming design processes,and managing clothing production.In China,the first undergraduate major in fashion design started in 1983 at Suzhou Silk Institute of Technology,which used to be linked to the Ministry of Textile Industry.The mode of teaching and learning delivery of fashion design courses just like other courses,started with face-to-face teaching in physical classroom and practical training classes.However,due to fast development in educational technology and movement restriction during COVID-19 pandemic,the fashion design education is adapting the innovative learning management system and revolutionized into online and hybrid delivery of teaching and learning content.In this review,fashion design courses in China education system will be elaborated and comparison of physical,online,and hybrid delivery of fashion design course will be discussed.
基金National Natural Science Foundation of China (No.60673010)Natural Science Foundation of Hubei Province ofChina (No.2009CDA135)
文摘With the prosperity of the Intemet, e-learning has been greatly improved. By supporting multiple learners and multiple roles in a learning activity, the IMS Leaming Design （LD） specification provides a collaborative scenario for participants. However, IMS LD provides insufficient support for interaction among learning activities and can not dynamically integrate learning resources to meet the continually changing service requirements. In this paper, a Business Process Execution Language （BPEL） enhanced requirement driven learning management architecture to address the issues of personalize adaptive learning was proposed. It models the learning activity by combining IMS LD with BPEL and matches optimal learning sequence based on Case-based reasoning （CBR） method. By providing expandable secure learning sequences flexibly, it satisfies the different actual demands for personalize learning
