The rapid changes and increased complexity in today’s world present new challenges and put new demands on the education system. There has been generally a growing awareness of the necessity?to change and improve the ...The rapid changes and increased complexity in today’s world present new challenges and put new demands on the education system. There has been generally a growing awareness of the necessity?to change and improve the existing system towards online learning. Jordan is one of the distinguished countries in the Middle East with rapid progress in education and with advanced teaching and learning technologies. The University of Jordan is trying to exploit Information and Communication Technology (ICT) in education and moving forward by introducing the latest E-learning management systems (LMSs) to keep pace of technological revolution in the higher education. It is?important to find out the impact of E-learning management system in the University of Jordan,?examine the students’ acceptance for this new system and address the challenges facing the students while using the E-learning management system and these are what this paper is trying to do.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
In this paper we introduce the e-society cognitive approach based on the social agent. The social Agent is e-learning oriented. The e-society cognitive platform may consider different fields like e-learning, e-health,...In this paper we introduce the e-society cognitive approach based on the social agent. The social Agent is e-learning oriented. The e-society cognitive platform may consider different fields like e-learning, e-health, e-commerce, e-medicine, and e-government. In this paper we will introduce the e-society platform. The e-society platform supports the educational and pedagogical aspects. The e-society is based on the agent technologies. The social agents offer impressive, meaningful and several features as autonomy, manage ne-gotiation, and make decision. The e-society cognitive platform consists of three main layers: social agents, beliefs, and tools for application layer. The goal of the e-society platform is to increase the perceiving of the transportation education in the school.展开更多
文摘The rapid changes and increased complexity in today’s world present new challenges and put new demands on the education system. There has been generally a growing awareness of the necessity?to change and improve the existing system towards online learning. Jordan is one of the distinguished countries in the Middle East with rapid progress in education and with advanced teaching and learning technologies. The University of Jordan is trying to exploit Information and Communication Technology (ICT) in education and moving forward by introducing the latest E-learning management systems (LMSs) to keep pace of technological revolution in the higher education. It is?important to find out the impact of E-learning management system in the University of Jordan,?examine the students’ acceptance for this new system and address the challenges facing the students while using the E-learning management system and these are what this paper is trying to do.
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In this paper we introduce the e-society cognitive approach based on the social agent. The social Agent is e-learning oriented. The e-society cognitive platform may consider different fields like e-learning, e-health, e-commerce, e-medicine, and e-government. In this paper we will introduce the e-society platform. The e-society platform supports the educational and pedagogical aspects. The e-society is based on the agent technologies. The social agents offer impressive, meaningful and several features as autonomy, manage ne-gotiation, and make decision. The e-society cognitive platform consists of three main layers: social agents, beliefs, and tools for application layer. The goal of the e-society platform is to increase the perceiving of the transportation education in the school.
