After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation ...After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data,making it an attractive resource for predicting student performance.In this study,we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets.The stacking method was employed for modeling in this research.The proposed model utilized weak learners,including nearest neighbor,decision tree,random forest,enhanced gradient,simple Bayes,and logistic regression algorithms.After a trial-and-error process,the logistic regression algorithm was selected as the final learner for the proposed model.The results of experiments with the above algorithms are reported separately for the pass and fail classes.The findings indicate that the accuracy of the proposed model on the OULAD dataset reached 98%.Overall,the proposed method improved accuracy by 4%on the OULAD dataset.展开更多
E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analyt...E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.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After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data,making it an attractive resource for predicting student performance.In this study,we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets.The stacking method was employed for modeling in this research.The proposed model utilized weak learners,including nearest neighbor,decision tree,random forest,enhanced gradient,simple Bayes,and logistic regression algorithms.After a trial-and-error process,the logistic regression algorithm was selected as the final learner for the proposed model.The results of experiments with the above algorithms are reported separately for the pass and fail classes.The findings indicate that the accuracy of the proposed model on the OULAD dataset reached 98%.Overall,the proposed method improved accuracy by 4%on the OULAD dataset.
基金The authors thank to the deanship of scientific research at Shaqra University for funding this research work through the Project Number(SU-ANN-2023017).
文摘E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.
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。
