十月底的北京，气温已经转低，暖气却尚未供应，正是室内最冷的时节。然而此时，北京交通大学的一间教室里却暖融融一片——十几名学生围坐在几台电脑前，聚精会神地听着一个大音箱里传出的声音，眼睛紧盯着电脑屏幕，不时小声地讨论几...十月底的北京，气温已经转低，暖气却尚未供应，正是室内最冷的时节。然而此时，北京交通大学的一间教室里却暖融融一片——十几名学生围坐在几台电脑前，聚精会神地听着一个大音箱里传出的声音，眼睛紧盯着电脑屏幕，不时小声地讨论几句，偶尔还敲打几下键盘——这是记者在北交大教育技术专业研究室里看到的一幕。“同学们正在收听美国教学设计专家M. David Merrill教授的讲座，展开更多
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
文摘十月底的北京，气温已经转低，暖气却尚未供应，正是室内最冷的时节。然而此时，北京交通大学的一间教室里却暖融融一片——十几名学生围坐在几台电脑前，聚精会神地听着一个大音箱里传出的声音，眼睛紧盯着电脑屏幕，不时小声地讨论几句，偶尔还敲打几下键盘——这是记者在北交大教育技术专业研究室里看到的一幕。“同学们正在收听美国教学设计专家M. David Merrill教授的讲座，
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.
