Potentialities of Artificial Intelligence in Higher Education: An Approach from Personalization
DOI:
https://doi.org/10.37843/rted.v14i1.296
Article Sidebar
Main Article Content
Abstract
There is a significant interest in knowing the educational processes and their actors in the case of research in the academic and pedagogical fields. The objective of this study was to analyze the potential of AI tools in higher education, taking into account an approach from the personalization of learning. This research was conducted under the empirical-analytical method, positivist paradigm, exploratory type, and documentary design. The population or sample considered were four databases (Scopus, Web of Science (Wos), Dialnet, and Redalyc). The technique used was a documentary observation, and the instrument used was the content sheet. The data analysis was carried out through the analysis matrix of the categories. The documents that did not answer the research questions proposed for this review were filtered with Boolean operators. In light of the results obtained, it is essential to consider the importance of contrasting pedagogical and curricular models concerning personalization. It is important to remember that a system with high technical content but little pedagogical content will deter students from using it. As a contribution to future research, it is recommended to consider the pedagogical and curricular models in the construction of personalization models. In addition, a contract should be made between the methodologies available in the literature to assess strengths and weaknesses.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Those authors who have publications in our journal accept the following terms:
- When a work is accepted for publication, the author retains rights of reproduction, distribution of his/her article for exploitation in all countries of the world in the format provided by our magazine and any other magnetic medium, optical, and digital.
- Authors will retain their copyright and guarantee the journal the right first to publish their work, which will be simultaneously subject to the Creative Commons Acknowledgment License (Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)). That allows third parties to copy and redistribute the material in any medium or format, under the following conditions: Acknowledgment - You must properly acknowledge authorship, provide a link to the license, and indicate if any changes have been made. You may do so in any reasonable way, but not in a way that suggests you have the licensor's endorsement or receive it for your use. NonCommercial - You may not use the material for a commercial purpose. NoDerivatives - If you remix, transform, or build from the material, you cannot broadcast the modified material. There are no additional restrictions - You cannot apply legal terms or technological measures that legally restrict you from doing what the license allows.
- Authors may adopt other non-exclusive license agreements to distribute the published version of the work (e.g., deposit it in an institutional archive or publish it in a monographic volume) provided that the initial publication in this journal is indicated.
- Authors are allowed and recommended to disseminate their work through the Internet (e.g., in institutional telematic archives, repositories, libraries, or their website), producing exciting exchanges and increasing the published work's citations.
- Request of withdrawal an article has to be done in writing by the author to the Editor, becoming effective after a written response from the Editor. For this purpose, the author or authors will send correspondence via e-mail: [email protected].
- The author will not receive financial compensation for the publication of his work.
- All Docentes 2.0 Journal publications are under the Open Journal System (OJS) platform at: https://ojs.docentes20.com/.
References
Abdullah, M., Bayahya, A. Y., Ba Shammakh, E. S., Altuwairqi, K. A., &
Alsaadi, A. A. (2017). A novel adaptive e-learning model matching educator-student learning styles based on machine learning. Communication, Management, and Information Technology - Proceedings of the International Conference on Communication, Management and Information Technology, ICCMIT 2016, 773–782. https://doi.org/10.1201/9781315375083-109
Aragón-García, M. (2016). Correlación inherente de los estilos del aprendizaje y las estrategias de enseñanza- aprendizaje. Revista Iberoamericana de Producción Académica y Gestión Educativa, 4, 1–16. http://pag.org.mx/index.php/PAG/article/view/586
Bouzenada, S. N. E., Zarour, N. E., & Boissier, O. (2018). An agent-based approach for personalised and adaptive learning. International Journal of Technology Enhanced Learning, 10(3), 184. https://doi.org/10.1504/ijtel.2018.10010193 DOI: https://doi.org/10.1504/IJTEL.2018.092701
Caro, M. F., Josyula, D. P., & Jiménez, J. A. (2015). Modelo pedagógico multinivel para la personalización de estrategias pedagógicas en sistemas tutoriales inteligentes. DYNA (Colombia), 82(194), 185–193. https://doi.org/10.15446/dyna.v82n194.49279 DOI: https://doi.org/10.15446/dyna.v82n194.49279
Flórez-Ochoa, R. (2000). Evaluación, pedagógica y cognición. McGraw-Hill.
Garzuzi, V. (2013). El desarrollo de estrategias de aprendizaje durante las trayectorias estudiantiles universitarias. Revista de Orientación Educacional, 27(51), 67–86. https://doi.org/10.1017/CBO9781107415324.004 DOI: https://doi.org/10.1017/CBO9781107415324.004
González, R. A., & D’Ancona, M. A. C. (1997). Metodología Cuantitativa. Estrategias y técnicas de investigación social. Reis, 80, 240. https://doi.org/10.2307/40183928 DOI: https://doi.org/10.2307/40183928
Ibáñez-Bernal, C. (2007). Un análisis crítico del modelo del triángulo pedagógico: Una propuesta alternativa. Revista Mexicana de Investigación Educativa, 12(32), 435–456. https://www.redalyc.org/pdf/140/14003220.pdf
Karagiannis, I., & Satratzemi, M. (2018). An adaptive mechanism for Moodle based on automatic detection of learning styles. Education and Information Technologies, 23(3), 1331–1357. https://doi.org/10.1007/s10639-017-9663-5 DOI: https://doi.org/10.1007/s10639-017-9663-5
Lerís-López, D., Sein-Echaluce, M., Hernández, M., & Fidalgo-Blanco, Á. (2016). Participantes heterogéneos en MOOCs y sus necesidades de aprendizaje adaptativo. Education in the Knowledge Society (EKS), 17(4), 91. https://doi.org/10.14201/eks201617491109 DOI: https://doi.org/10.14201/eks201617491109
Maraza-Quispe, B., Oviedo, O., Cisneros-Chavez, B., Cuentas-Toledo, M., Cuadros-Paz, L., Fernandez-Gambarini, W., Quispe-Flores, L., & Caytuiro-Silva, N. (2019). Model to personalize the teaching-learning process in virtual environments using case-based reasoning. ACM International Conference Proceeding Series, 105–110. https://doi.org/10.1145/3369255.3369264 DOI: https://doi.org/10.1145/3369255.3369264
Raj, N. S., & Renumol, V. G. (2021). A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. Journal of Computers in Education. https://doi.org/10.1007/s40692-021-00199-4 DOI: https://doi.org/10.1007/s40692-021-00199-4
Rincón-Flores, E. G., Mena, J., López-Camacho, E., & Olmos, O. (2019). Adaptive learning based on AI with predictive algorithms. ACM International Conference Proceeding Series, 607–612. https://doi.org/10.1145/3362789.3362869 DOI: https://doi.org/10.1145/3362789.3362869
Rivero-Albarrán, D. M., Ulloa-Erazo, N. G., Guerra, L. R. T., Arellano, B., & Arciniegas,S. M. A. (2019). Agente adaptativo para la enseñanza en ambientes inteligentes. Revista Ibérica de Sistemas e Tecnologias de Informação, (19), 694–707. https://bit.ly/3GWNevj
Rodríguez, N. (2004). Criterios para el Análisis del Diseño Curricular. Educación, Aprendizaje y Cognición. Teoría En La Práctica, 107–122. http://servicio.bc.uc.edu.ve/educacion/revista/n45/art06.pdf
Supangat, M. B. S. (2020). Development of e-learning system using felder and silverman’s index of learning styles model. International Journal of Advanced Trends in Computer Science and Engineering, 9(5), 8554–8561. https://doi.org/10.30534/ijatcse/2020/236952020 DOI: https://doi.org/10.30534/ijatcse/2020/236952020
Tetzlaff, L., Schmiedek, F., & Brod, G. (2021). Developing Personalized Education: A Dynamic Framework. Educational Psychology Review, 33(3), 863–882. https://doi.org/10.1007/s10648-020-09570-w DOI: https://doi.org/10.1007/s10648-020-09570-w
Únete a nuestro canal de Telegram para recibir notificaciones de nuestras publicaciones
