How to share experience and resources among learners is becoming one of the hottest topics in the field of E-Learning collaborative techniques. An intuitive way to achieve this objective is to group learners which can...How to share experience and resources among learners is becoming one of the hottest topics in the field of E-Learning collaborative techniques. An intuitive way to achieve this objective is to group learners which can help each other into the same community and help them learn collaboratively. In this paper, we proposed a novel community self-organization model based on multi-agent mechanism, which can automatically group learners with similar preferences and capabilities. In particular, we proposed award and exchange schemas with evaluation and preference track records to raise the performance of this algorithm. The description of learner capability, the matchmaking process, the definition of evaluation and preference track records, the rules of award and exchange schemas and the self-organization algorithm are all discussed in this paper. Meanwhile, a prototype has been built to verify the validity and efficiency of the algorithm. Experiments based on real learner data showed that this mechanism can organize learner communities properly and efficiently; and that it has sustainable improved efficiency and scalability.展开更多
Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications.At the same time,machine learning(ML)and data min...Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications.At the same time,machine learning(ML)and data mining approaches are presented for accomplishing prediction and classification processes.With this motivation,this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection(IML-ELNVBD)in IoT environment.The proposed IML-ELNVBD technique allows the IoT devices such as audio sensors,cameras,etc.which are then connected to the cloud server for further processing.In addition,the modelling and extraction of behaviour take place.Moreover,extreme learning machine sparse autoencoder(ELM-SAE)model is employed for the detection and classification of non-verbal behaviour.Finally,the Ant Colony Optimization(ACO)algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE model.In order to ensure the improved performance of the IML-ELNVBD model,a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.展开更多
E-Learning自诞生以来,受到商业组织和学术机构的广泛关注。利用共词分析、聚类分析、社会网络分析、网络社区分析等方法对Web of Science数据库收录的E-Learning研究文献进行的深度剖析发现:国际E-Learning研究发展大体可以分为三个阶...E-Learning自诞生以来,受到商业组织和学术机构的广泛关注。利用共词分析、聚类分析、社会网络分析、网络社区分析等方法对Web of Science数据库收录的E-Learning研究文献进行的深度剖析发现:国际E-Learning研究发展大体可以分为三个阶段:1968-1993年,E-Learning相关研究开始出现,但研究文献较少,以案例研究等定性研究方法为主,处于以概念探讨为主的初级研究阶段;1994-2003年,研究文献增多,研究方法逐渐偏向于定量研究,开始形成较为稳定的研究主题领域,并逐渐出现主题分化和主题融合;2004-2013年,研究主题不断细化,研究深度进一步增强。总体来看,国际E-Learning研究经历从"技术导向"到"行为导向"再到"行为和技术导向"相融合、从单一强调"学习者"或"教学者"的自我导向学习研究到同时强调"教学者"和"学习者"的互动协作学习研究等主题演化特征,跨文化、跨学科研究成为全球化背景下国际E-Learning研究方向,研究模型设计越来越注重中介变量和调节变量的作用。混合式网络学习环境研究、强调个性化和智能化的E-Learning系统研究、基于认知心理的学习效能研究可能成为未来E-Learning研究的潜在热点领域。展开更多
文摘How to share experience and resources among learners is becoming one of the hottest topics in the field of E-Learning collaborative techniques. An intuitive way to achieve this objective is to group learners which can help each other into the same community and help them learn collaboratively. In this paper, we proposed a novel community self-organization model based on multi-agent mechanism, which can automatically group learners with similar preferences and capabilities. In particular, we proposed award and exchange schemas with evaluation and preference track records to raise the performance of this algorithm. The description of learner capability, the matchmaking process, the definition of evaluation and preference track records, the rules of award and exchange schemas and the self-organization algorithm are all discussed in this paper. Meanwhile, a prototype has been built to verify the validity and efficiency of the algorithm. Experiments based on real learner data showed that this mechanism can organize learner communities properly and efficiently; and that it has sustainable improved efficiency and scalability.
文摘Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications.At the same time,machine learning(ML)and data mining approaches are presented for accomplishing prediction and classification processes.With this motivation,this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection(IML-ELNVBD)in IoT environment.The proposed IML-ELNVBD technique allows the IoT devices such as audio sensors,cameras,etc.which are then connected to the cloud server for further processing.In addition,the modelling and extraction of behaviour take place.Moreover,extreme learning machine sparse autoencoder(ELM-SAE)model is employed for the detection and classification of non-verbal behaviour.Finally,the Ant Colony Optimization(ACO)algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE model.In order to ensure the improved performance of the IML-ELNVBD model,a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.
文摘E-Learning自诞生以来,受到商业组织和学术机构的广泛关注。利用共词分析、聚类分析、社会网络分析、网络社区分析等方法对Web of Science数据库收录的E-Learning研究文献进行的深度剖析发现:国际E-Learning研究发展大体可以分为三个阶段:1968-1993年,E-Learning相关研究开始出现,但研究文献较少,以案例研究等定性研究方法为主,处于以概念探讨为主的初级研究阶段;1994-2003年,研究文献增多,研究方法逐渐偏向于定量研究,开始形成较为稳定的研究主题领域,并逐渐出现主题分化和主题融合;2004-2013年,研究主题不断细化,研究深度进一步增强。总体来看,国际E-Learning研究经历从"技术导向"到"行为导向"再到"行为和技术导向"相融合、从单一强调"学习者"或"教学者"的自我导向学习研究到同时强调"教学者"和"学习者"的互动协作学习研究等主题演化特征,跨文化、跨学科研究成为全球化背景下国际E-Learning研究方向,研究模型设计越来越注重中介变量和调节变量的作用。混合式网络学习环境研究、强调个性化和智能化的E-Learning系统研究、基于认知心理的学习效能研究可能成为未来E-Learning研究的潜在热点领域。
