The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach
The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Education Sciences |
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| Online Access: | https://www.mdpi.com/2227-7102/10/10/270 |
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| author | Dang-Nhac Lu Hong-Quang Le Tuan-Ha Vu |
| author_facet | Dang-Nhac Lu Hong-Quang Le Tuan-Ha Vu |
| author_sort | Dang-Nhac Lu |
| collection | DOAJ |
| description | The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%. |
| first_indexed | 2024-03-10T15:57:09Z |
| format | Article |
| id | doaj.art-bdbc9432cc1b49ada757a253fd9d084d |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-10T15:57:09Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-bdbc9432cc1b49ada757a253fd9d084d2023-11-20T15:37:11ZengMDPI AGEducation Sciences2227-71022020-09-01101027010.3390/educsci10100270The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm ApproachDang-Nhac Lu0Hong-Quang Le1Tuan-Ha Vu2Academy of Journalism and Communication, 36 Xuan Thuy Street, Cau Giay District, Hanoi 123105, VietnamAcademy of Journalism and Communication, 36 Xuan Thuy Street, Cau Giay District, Hanoi 123105, VietnamAcademy of Journalism and Communication, 36 Xuan Thuy Street, Cau Giay District, Hanoi 123105, VietnamThe Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%.https://www.mdpi.com/2227-7102/10/10/270factors affectinge-learning systemmachine learning for e-learning |
| spellingShingle | Dang-Nhac Lu Hong-Quang Le Tuan-Ha Vu The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach Education Sciences factors affecting e-learning system machine learning for e-learning |
| title | The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach |
| title_full | The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach |
| title_fullStr | The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach |
| title_full_unstemmed | The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach |
| title_short | The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach |
| title_sort | factors affecting acceptance of e learning a machine learning algorithm approach |
| topic | factors affecting e-learning system machine learning for e-learning |
| url | https://www.mdpi.com/2227-7102/10/10/270 |
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