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
<div style="text-align:justify;"> <span style="font-family:Verdana;">Supporting higher education with modern technologies like E-Learning is very important for one country to improve qu...<div style="text-align:justify;"> <span style="font-family:Verdana;">Supporting higher education with modern technologies like E-Learning is very important for one country to improve quality of education, to meet student’s expectations and to continue teaching-learning and training when face to face education is impossible. However, it is in its preliminary stage in developing countries like Ethiopia. This study examined the enabling factors, difficulties and opportunities of E-Learning implementation in Assosa University (ASU), Ethiopia. Its purpose is to find the enabling factors, difficulties and opportunities of E-Learning implementation in ASU and developing prototype of E-Learning system to show its practicality and to identify approaches of students’ and lecturers towards E-Learning. The study employed questionnaires, observation and interview to gather the required information. A sample of 309 students and 64 Lecturers randomly selected from 7 colleges and two schools as well as ICT workers and other concerned bodies in the university. Also prototyping as a methodology was used to implement and test the proposed system for proof of concept. This study investigating the possibility of implementing E-Learning in ASU and important enablers, difficulties and opportunities is identified. Also the E-Learning platform of the university is developed and introduced for students and lecturers to show its practicality. Most students and lecturers showed good motivation in E-Learning implementation and they assumed that it is useful for the university. The result shows that although there are difficulties to implement E-Learning in ASU, the possibility of fully implementing E-Learning in the University is relatively high with mixed method. With this, the approaches of students and lecturers are positively viewed and the opportunities are very noticeable in the University. So, conventional higher education can practically implement E-Learning with mixed approach to use as supportive tool for educational improvements and to reduce physical presence.</span> </div>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<div style="text-align:justify;"> <span style="font-family:Verdana;">Supporting higher education with modern technologies like E-Learning is very important for one country to improve quality of education, to meet student’s expectations and to continue teaching-learning and training when face to face education is impossible. However, it is in its preliminary stage in developing countries like Ethiopia. This study examined the enabling factors, difficulties and opportunities of E-Learning implementation in Assosa University (ASU), Ethiopia. Its purpose is to find the enabling factors, difficulties and opportunities of E-Learning implementation in ASU and developing prototype of E-Learning system to show its practicality and to identify approaches of students’ and lecturers towards E-Learning. The study employed questionnaires, observation and interview to gather the required information. A sample of 309 students and 64 Lecturers randomly selected from 7 colleges and two schools as well as ICT workers and other concerned bodies in the university. Also prototyping as a methodology was used to implement and test the proposed system for proof of concept. This study investigating the possibility of implementing E-Learning in ASU and important enablers, difficulties and opportunities is identified. Also the E-Learning platform of the university is developed and introduced for students and lecturers to show its practicality. Most students and lecturers showed good motivation in E-Learning implementation and they assumed that it is useful for the university. The result shows that although there are difficulties to implement E-Learning in ASU, the possibility of fully implementing E-Learning in the University is relatively high with mixed method. With this, the approaches of students and lecturers are positively viewed and the opportunities are very noticeable in the University. So, conventional higher education can practically implement E-Learning with mixed approach to use as supportive tool for educational improvements and to reduce physical presence.</span> </div>