Análisis sobre las plataformas LMS considerando el deep learning y random forest
DOI:
https://doi.org/10.12795/revistafuentes.2024.24123Palabras clave:
Tecnología, Enseñanza, Tecnología de la información, Método de enseñanza, Educación, Aprendizaje, Tecnología educacional, Enseñanza multimediaResumen
Hoy en día, los educadores se apoyan en la tecnología para ofrecer a los estudiantes nuevos ambientes de aprendizaje. El objetivo de este estudio mixto es analizar la percepción de los estudiantes sobre el uso de las plataformas LMS durante la postpandemia COVID-19 por medio los algoritmos deep learning y random forest. Los resultados del algoritmo deep learning señalan que el uso de las plataformas LMS afecta positivamente el análisis y uso de la información escolar, la autonomía y el intercambio de ideas durante el proceso de aprendizaje. Asimismo, el algoritmo random forest permitió la construcción de tres modelos sobre esta herramienta tecnológica considerando el perfil de los estudiantes. Las limitaciones de esta investigación cuantitativa y cualitativa son la muestra y las variables dependientes. Por consiguiente, las futuras investigaciones pueden analizar el uso de las plataformas LMS durante la postpandemia COVID-19 considerando el rol activo de los estudiantes, el desarrollo de habilidades y la asimilación del conocimiento en diversas escuelas, facultades e institutos. Las implicaciones de este estudio están relacionadas con el uso de las plataformas LMS para eliminar las barreras físicas, fomentar el aprendizaje personalizado y actualizar las actividades bajo la modalidad a distancia. En conclusión, los educadores deben de incluir las plataformas LMS en la planeación de los cursos con el propósito de facilitar el proceso de aprendizaje.
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Aitdaoud, M., Namir, A., & Talbi, M. (2023). A New Pre-Processing Approach Based on Clustering Users Traces According to their Learning Styles in Moodle LMS. International Journal of Emerging Technologies in Learning, 18(7), 226-242. https://doi.org/10.3991/ijet.v18i07.37635
Akay, E., & Koral-Gumusoglu, E. (2020). The impact of learning management systems on students’ achievement in language exams. Turkish Online Journal of Distance Education, 21(4), 206-222. https://doi.org/10.17718/tojde.803410
Al-Breiki, M., & Al-Abri, A. (2022). The Extended Technology Acceptance Model (ETAM): Examining Students’ Acceptance of Online Learning During COVID-19 Pandemic. International Journal of Emerging Technologies in Learning, 17(20), 4-19. https://doi.org/10.3991/ijet.v17i20.29441
Aldosemani, T., Shepherd, C. E. & Bolliger, D. U. (2019). Perceptions of Instructors Teaching in Saudi Blended Learning Environments. TechTrends, 63, 341-352. https://doi.org/10.1007/s11528-018-0342-1
Alkabaa, A. S. (2022). Effectiveness of using E-learning systems during COVID-19 in Saudi Arabia: Experiences and perceptions analysis of engineering students. Education and Information Technologies, 27, 10625-10645. https://doi.org/10.1007/s10639-022-11054-z
Alserhan, S., & Yahaya, N. (2021). Teachers’ Perspective on Personal Learning Environments via Learning Management Systems Platform. International Journal of Emerging Technologies in Learning, 16(24), 57-73. https://doi.org/10.3991/ijet.v16i24.27433
Altinpulluk, H. & Kesim, M. (2021). A systematic review of the tendencies in the use of learning management systems. Turkish Online Journal of Distance Education, 22(3), 40-54. https://doi.org/10.17718/tojde.961812
Alturise, F. (2020). Evaluation of Blackboard Learning Management System for Full Online Courses in Western Branch Colleges of Qassim University. International Journal of Emerging Technologies in Learning, 15(15), 33-51. https://doi.org/10.3991/ijet.v15i15.14199
Binyamin, S. S., Rutter, M., & Smith, S. (2019). Extending the Technology Acceptance Model to Understand Students’ Use of Learning Management Systems in Saudi Higher Education. International Journal of Emerging Technologies in Learning, 14(3), 4-21. https://doi.org/10.3991/ijet.v14i03.9732
Castañeda, L., & Villar-Onrubia, D. (2023). Beyond functionality: Building critical digital teaching competence among future primary education teachers. Contemporary Educational Technology, 15(1), ep397. https://doi.org/10.30935/cedtech/12599
Chadaga, K., Prabhu, S., Vivekananda, B., Niranjana, S., & Umakanth, S. (2021) Battling COVID-19 using machine learning: A review. Cogent Engineering, 8(1), 1-12. https://doi.org/10.1080/23311916.2021.1958666
Darma-Kotama, I. N., Oka-Saputra, K., & Linawati, L. (2019). Proposed Model of Multiplayer Matching Game Plugins Using Websocket in Moodle. International Journal of Emerging Technologies in Learning, 14(11), 194-201. https://doi.org/10.3991/ijet.v14i11.10190
Demir, F., Bruce-Kotey, C. & Alenezi, F. (2022). User Experience Matters: Does One size Fit all? Evaluation of Learning Management Systems. Technology, Knowledge and Learning, 27, 49-67. https://doi.org/10.1007/s10758-021-09518-1
Fauszt, T., Erdélyi, K., Dobák, D., Bognár, L., & Kovács, E. (2023). Design of a Machine Learning Model to Predict Student Attrition. International Journal of Emerging Technologies in Learning, 18(17), 184-195. https://doi.org/10.3991/ijet.v18i17.41449
Gamage, S. H., Ayres, J. R., & Behrend, M. B. (2022). A systematic review on trends in using Moodle for teaching and learning. International Journal of STEM Education, 9, 1-9. https://doi.org/10.1186/s40594-021-00323-x
Green, K. R., & Chewning, H. L. (2020). The Fault in our Systems: LMS as a Vehicle for Critical Pedagogy. TechTrends, 64, 423-431. https://doi.org/10.1007/s11528-020-00480-w
Kassymova, G. M., Tulepova, S. B., & Bekturova, M. B. (2023). Perceptions of digital competence in learning and teaching English in the context of online education. Contemporary Educational Technology, 15(1), ep396. https://doi.org/10.30935/cedtech/12598
Korotaeva, I., & Kapustina, D. (2022). Specific Features of the Use of Distance Learning Technologies in Foreign Language Classes with Postgraduate Students. International Journal of Emerging Technologies in Learning, 17(20), 20-33. https://doi.org/10.3991/ijet.v17i20.30305
Koyuncu, I., Kilic, A. F. & Orhan-Goksun, D. (2022). Classification of students’ achievement via machine learning by using system logs in learning management system. Turkish Online Journal of Distance Education, 23(3), 18-30. https://doi.org/10.17718/tojde.1137114
Lamsyah, R., El Bouazzaoui, A., & Kanjaa, N. (2022). Impact of the Flipped Classroom on the Motivation of Undergraduate Students of the Higher Institute of Nursing Professions and Health Techniques of Fez-Morocco. International Journal of Emerging Technologies in Learning, 17(22), 39-60. https://doi.org/10.3991/ijet.v17i22.33365
Leshchenko, M., Lavrysh, Y., Halatsyn, K., Feshchuk, A., & Prykhodko, D. (2023). Technology-Enhanced Personalized Language Learning: Strategies and Challenges. International Journal of Emerging Technologies in Learning, 18(13), 120-136. https://doi.org/10.3991/ijet.v18i13.39905
Li, R., & Meng, Y. (2023). Factors Influencing the Quality of Online Teaching: Application of DEMATEL and Cluster Technology. International Journal of Emerging Technologies in Learning, 18(13), 163-177. https://doi.org/10.3991/ijet.v18i13.40393
Mohamed-Amin, M., & Paiman, N. (2022). University English Language Teachers’ Use of Digital Platforms for Online Teaching. International Journal of Emerging Technologies in Learning, 17(20), 134-148. https://doi.org/10.3991/ijet.v17i20.31421
Mpungose, C. B., & Khoza, S. B. (2022). Postgraduate Students’ Experiences on the Use of Moodle and Canvas Learning Management System. Technology, Knowledge and Learning, 27, 1-16. https://doi.org/10.1007/s10758-020-09475-1
Ortega-Ruipérez, B. (2022). Use of Self-Assessment Questionaries with Moodle Surveys to Improve Reading Comprehension and Study Habits in Preservice Teacher Education. International Journal of Emerging Technologies in Learning, 17(20), 200-212. https://doi.org/10.3991/ijet.v17i20.32715
Ozkan, U. B., Cigdem, H. & Erdogan, T. (2020). Artificial neural network approach to predict lms acceptance of vocational school students. Turkish Online Journal of Distance Education, 21(3), 156-169. https://doi.org/10.17718/tojde.762045
Pham, D. T., & Nguyen, T. H. H. (2023). University Students’ Perceptions of Google Tools in Learning English Courses Online. International Journal of Emerging Technologies in Learning, 18(13), 45-61. https://doi.org/10.3991/ijet.v18i13.39857
Prahani, B. K., Alfin, J., Fuad, A. Z., Saphira, H. V., Hariyono, E., & Suprapto, N. (2022). Learning Management System (LMS) Research During 1991-2021: How Technology Affects Education. International Journal of Emerging Technologies in Learning, 17(17), 28-49. https://doi.org/10.3991/ijet.v17i17.30763
Qin, S., Orchakova, L., Liu, Z. Y., Smirnova, Y., & Tokareva, E. (2022). Using the Learning Management System Modular Object-Oriented Dynamic Learning Environment in Multilingual Education. International Journal of Emerging Technologies in Learning, 17(3), 173-191. https://doi.org/10.3991/ijet.v17i03.25851
Rabayah, K. S., & Amira, N. (2022). Learners’ engagement assessment in e-learning during the COVID-19 pandemic: nation-wide exploration. Education and Information Technologies, 27, 10647-10663. https://doi.org/10.1007/s10639-022-11006-7
Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., Rodríguez-Díez, J. J., Rodríguez-Arribas, S., Díez-Pastor, J. F., & Ji, Y. P. (2021). Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques. Journal of Computing in Higher Education, 33, 747-778. https://doi.org/10.1007/s12528-021-09289-9
Salas-Rueda, R. A. (2023). Uso del deep learning para analizar Facebook y Google Classroom en el campo educativo. Pixel-Bit. Revista De Medios y Educación, 67, 87-122. https://doi.org/10.12795/pixelbit.96994
Saleh, A. M., Abuaddous, H. Y., Alansari, I. S., & Enaizan, O. (2022). The Evaluation of User Experience on Learning Management Systems Using UEQ. International Journal of Emerging Technologies in Learning, 17(7), 145-162. https://doi.org/10.3991/ijet.v17i07.29525
Sarker, M. F. H., Rahman, S. M., Khan, S., Sohel, M. S., Tamal, M. A., Khan, M. M. R., & Islam, M. K. (2023). Perception and Preference of the Students for Online Education during COVID-19 in Bangladesh: A Study Based on Binary Logistic Regression. International Journal of Emerging Technologies in Learning, 18(13), 74-90. https://doi.org/10.3991/ijet.v18i13.3880
Shurygin, V., Berestova, A., Litvinova, T., Kolpak, E., & Nureyeva, A. (2021). Universal Models and Platforms in E-Learning. International Journal of Emerging Technologies in Learning, 16(9), 63-75. https://doi.org/10.3991/ijet.v16i09.19697
Su, C. Y., Li, Y. H., & Chen, C. H. (2021). Understanding the Behavioural Patterns of University Teachers Toward Using a Learning Management System. International Journal of Emerging Technologies in Learning, 16(14), 129-145. https://doi.org/10.3991/ijet.v16i14.22685
Sulaymani, O., Pratama, A. R., Alshaikh, M., & Alammary, A. (2022). The Effects of Previous Experience and Self Efficacy on the Acceptance of e-Learning Platforms Among Younger Students in Saudi Arabia. Contemporary Educational Technology, 14(2), ep349. https://doi.org/10.30935/cedtech/11524
Sun, Y., Huang, W., Wang, Z., Xu, X., Wen, M., & Wu, P. (2023). Smart Teaching Systems: A Hybrid Framework of Reinforced Learning and Deep Learning. International Journal of Emerging Technologies in Learning, 18(20), 37-50. https://doi.org/10.3991/ijet.v18i20.44217
Tinmaz, H. & Lee, J. H. (2020). An analysis of users’ preferences on learning management systems: a case on German versus Spanish students. Smart Learning Environments, 7, 30. https://doi.org/10.1186/s40561-020-00141-8
Verawati, N., Ernita, N., & Prayogi, S. (2022). Enhancing the Reasoning Performance of STEM Students in Modern Physics Courses Using Virtual Simulation in the LMS Platform. International Journal of Emerging Technologies in Learning, 17(13), 267-277. https://doi.org/10.3991/ijet.v17i13.31459
Yalley, A. A. (2022). Student readiness for e-learning co-production in developing countries higher education institutions. Education and Information Technologies, 27, 12421-12448. https://doi.org/10.1007/s10639-022-11134-0
Yamani, H. A., Alharthi, A. D., & Smirani, L. K. (2022). Evaluation of Learning Management Systems: A Comparative Study Between Blackboard and Brightspace. International Journal of Emerging Technologies in Learning, 17(7), 125-144. https://doi.org/10.3991/ijet.v17i07.28881
Zacharis, G., & Nikolopoulou, K. (2022). Factors predicting University students’ behavioral intention to use eLearning platforms in the post-pandemic normal: an UTAUT2 approach with ‘Learning Value’. Education and Information Technologies, 27, 12065-12082. https://doi.org/10.1007/s10639-022-11116-2
Zhang, Y. (2022). Exploring Students’ Increased Use of Tablets After Taking Online Courses During the COVID-19 Lockdown. Contemporary Educational Technology, 14(4), ep380. https://doi.org/10.30935/cedtech/12283
Zuev, V., Kakisheva, L., Denissova, N., Kumargazhanova, S., & Smailova, S. (2021). Development of a Set of Requirements for the Hardware and Software of LMS Services of the University. International Journal of Emerging Technologies in Learning, 16(21), 210-218. https://doi.org/10.3991/ijet.v16i21.25239
Publicado
2024-05-15
Cómo citar
Salas-Rueda, R.-A. (2024). Análisis sobre las plataformas LMS considerando el deep learning y random forest. Revista Fuentes, 26(2), 134–146. https://doi.org/10.12795/revistafuentes.2024.24123
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Recibido 2023-07-09
Aceptado 2023-11-03
Publicado 2024-05-15
Aceptado 2023-11-03
Publicado 2024-05-15
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