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
目前,DNA甲基化已成为生命科学和医学领域中备受瞩目的研究热点之一,与之相关的文献数量呈现出快速增长的趋势。为深入理解DNA甲基化研究的整体发展历程与趋势,本研究采用文献分析方法,对近30年间Web of Science(WOS)数据库和CNKI数据...目前,DNA甲基化已成为生命科学和医学领域中备受瞩目的研究热点之一,与之相关的文献数量呈现出快速增长的趋势。为深入理解DNA甲基化研究的整体发展历程与趋势,本研究采用文献分析方法,对近30年间Web of Science(WOS)数据库和CNKI数据库中与DNA甲基化相关的文献进行了详细分析,并利用VOSviewer软件进行了可视化处理。检索和分析结果显示,WOS数据库中与DNA甲基化相关文献有21262篇,CNKI数据库有25664篇。在WOS中,美国相关论文数量占有绝对优势,且篇均被引频次和h-指数较高。研究方向主要集中在遗传学、生物化学与分子生物学、肿瘤学这3个方面。关键词主要为“表观遗传学(epigenetics)”、“基因表达(gene-expression)“和”癌症(cancer)”其中基因表达是DNA甲基化研究领域的热点和方向,这些关键词反映了DNA甲基化在这些领域的广泛应用和深入探索。对CNKI数据库分析的结果显示,研究论文主要发表在《癌症》、《中国畜牧兽医》及《畜牧兽医学报》等期刊。本研究通过对WOS和CNKI数据库中DNA甲基化相关文献的深入分析,为后续DNA甲基化领域的研究提供了有价值的信息和参考。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.
文摘目前,DNA甲基化已成为生命科学和医学领域中备受瞩目的研究热点之一,与之相关的文献数量呈现出快速增长的趋势。为深入理解DNA甲基化研究的整体发展历程与趋势,本研究采用文献分析方法,对近30年间Web of Science(WOS)数据库和CNKI数据库中与DNA甲基化相关的文献进行了详细分析,并利用VOSviewer软件进行了可视化处理。检索和分析结果显示,WOS数据库中与DNA甲基化相关文献有21262篇,CNKI数据库有25664篇。在WOS中,美国相关论文数量占有绝对优势,且篇均被引频次和h-指数较高。研究方向主要集中在遗传学、生物化学与分子生物学、肿瘤学这3个方面。关键词主要为“表观遗传学(epigenetics)”、“基因表达(gene-expression)“和”癌症(cancer)”其中基因表达是DNA甲基化研究领域的热点和方向,这些关键词反映了DNA甲基化在这些领域的广泛应用和深入探索。对CNKI数据库分析的结果显示,研究论文主要发表在《癌症》、《中国畜牧兽医》及《畜牧兽医学报》等期刊。本研究通过对WOS和CNKI数据库中DNA甲基化相关文献的深入分析,为后续DNA甲基化领域的研究提供了有价值的信息和参考。
