Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques
Education is one of the sectors that improves the future of societies; unfortunately, the pandemic generated by coronavirus disease 2019 has caused a variety of problems that directly affect learning. Universities have found it necessary to begin a transition towards remote or online educational mod...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/10/1192 |
| _version_ | 1797533815074193408 |
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| author | William Villegas-Ch. Joselin García-Ortiz Santiago Sánchez-Viteri |
| author_facet | William Villegas-Ch. Joselin García-Ortiz Santiago Sánchez-Viteri |
| author_sort | William Villegas-Ch. |
| collection | DOAJ |
| description | Education is one of the sectors that improves the future of societies; unfortunately, the pandemic generated by coronavirus disease 2019 has caused a variety of problems that directly affect learning. Universities have found it necessary to begin a transition towards remote or online educational models. To do so, the only method that guarantees the continuity of classes is using information and communication technologies. The transition in the foreground points to the use of technological platforms that allow interaction and the development of classes through synchronous sessions. In this way, it has been possible to continue developing both administrative and academic activities. However, in effective education, there are factors that create an ideal environment where the generation of knowledge is possible. By moving from traditional educational models to remote models, this environment has been disrupted, significantly affecting student learning. Identifying the factors that influence academic performance has become the priority of universities. This work proposes the use of intelligent techniques that allow the identification of the factors that affect learning and allow effective decision-making that allows improving the educational model. |
| first_indexed | 2024-03-10T11:21:00Z |
| format | Article |
| id | doaj.art-603dc7b7bd2340669b2d061667195bfa |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T11:21:00Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-603dc7b7bd2340669b2d061667195bfa2023-11-21T20:01:37ZengMDPI AGElectronics2079-92922021-05-011010119210.3390/electronics10101192Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence TechniquesWilliam Villegas-Ch.0Joselin García-Ortiz1Santiago Sánchez-Viteri2Escuela de Ingeniería en Tecnologías de la Información, FICA, Universidad de Las Américas, Quito 170125, EcuadorEscuela de Ingeniería en Tecnologías de la Información, FICA, Universidad de Las Américas, Quito 170125, EcuadorDepartamento de Sistemas, Universidad Internacional del Ecuador, Quito 170411, EcuadorEducation is one of the sectors that improves the future of societies; unfortunately, the pandemic generated by coronavirus disease 2019 has caused a variety of problems that directly affect learning. Universities have found it necessary to begin a transition towards remote or online educational models. To do so, the only method that guarantees the continuity of classes is using information and communication technologies. The transition in the foreground points to the use of technological platforms that allow interaction and the development of classes through synchronous sessions. In this way, it has been possible to continue developing both administrative and academic activities. However, in effective education, there are factors that create an ideal environment where the generation of knowledge is possible. By moving from traditional educational models to remote models, this environment has been disrupted, significantly affecting student learning. Identifying the factors that influence academic performance has become the priority of universities. This work proposes the use of intelligent techniques that allow the identification of the factors that affect learning and allow effective decision-making that allows improving the educational model.https://www.mdpi.com/2079-9292/10/10/1192artificial intelligenceremote educationWEKA |
| spellingShingle | William Villegas-Ch. Joselin García-Ortiz Santiago Sánchez-Viteri Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques Electronics artificial intelligence remote education WEKA |
| title | Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques |
| title_full | Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques |
| title_fullStr | Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques |
| title_full_unstemmed | Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques |
| title_short | Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques |
| title_sort | identification of the factors that influence university learning with low code no code artificial intelligence techniques |
| topic | artificial intelligence remote education WEKA |
| url | https://www.mdpi.com/2079-9292/10/10/1192 |
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