Andragogical Models Mediated by Artificial Intelligence in University Pedagogical Support
DOI:
https://doi.org/10.5281/zenodo.18273906Keywords:
andragogy, educational technology, autonomous learning, higher education, teaching innovation, digital tutoringAbstract
To determine the impact of andragogical models supported by artificial intelligence on the improvement of university pedagogical guidance. Explanatory quantitative study with a quasi-experimental design, including pretest-posttest measurements and a control group. A total of 120 undergraduate students (60 experimental group, 60 control group) from social sciences and humanities programs at private universities in North Lima participated, selected from a population of 847, with a margin of error of ±8.9% at 95% confidence. An andragogical model mediated by conversational AI was applied, assessing academic performance, learning autonomy, and student satisfaction using validated instruments. Significant improvements were observed in the experimental group, with a 23% increase in academic performance (p<0.001) and a 31% increase in perceived learning autonomy (p<0.001). The discussion indicated that technological mediation strengthens the andragogical principles of self-direction and prior experience, although challenges remain in faculty digital literacy. Conclusion: Andragogical models mediated by AI represent a viable option for democratizing university pedagogical guidance, particularly in contexts of educational expansion. The scientific contribution lies in proposing a theoretical-methodological framework that integrates andragogy and artificial intelligence applied to higher education.
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Akgun, S., & Greenhouse, J. B. (2022). Predictive analytics in education: A comparison of machine learning algorithms for student success. Journal of Educational Data Mining, Journal of Educational Data Mining. 14(1), 1-25. https://doi.org/10.5281/jedm.v14i1.567
Aljohani, N. R., Fayoumi, A., & Hassan, S. U. (2019). Predicting at-risk students using clickstream data in the virtual learning environment. Sustainability. Sustainability, 11(24), 7238. https://doi.org/10.3390/su11247238
Baber, H. (2021). Social interaction and effectiveness of the online learning: A moderating role of maintaining social distance during the pandemic COVID-19. Asian Education and Development Studies. Asian Education and Development Studies, 11(1), 159-171. https://doi.org/10.1108/AEDS-09-2020-0209
Bañeres, D., Rodríguez-Gonzalez, M. E., & Serra, M. (2020 An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Transactions on Learning Technologies, 12(2), 249-263. https://doi.org/10.1109/TLT.2019.2912167
Broadbent, J., Fuller-Tyszkiewicz, M., & Skladzien, E. (2020). The use of learning management system data to predict online learning engagement and academic performance: A systematic review. Educational Technology Research and Development, 68(5), 2823-2849. https://doi.org/10.1007/s11423-020-09795-w
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1-13. https://doi.org/10.1016/j.iheduc.2015.04.007
Cabero-Almenara, J., & Valencia-Ortiz, R. (2021). Y el COVID-19 transformó al sistema educativo: reflexiones y experiencias por aprender. International Journal of Educational Research and Innovation, 15, 218-228. https://doi.org/10.46661/ijeri.5246
Fernández-Pascual, M. D., Ferrer-Cascales, R., & Reig-Ferrer, A. (2021). Learning analytics para la predicción del rendimiento académico de los estudiantes en modalidad a distancia. Revista de Educación a Distancia, 21(65), 1-22. https://doi.org/10.6018/red.456211
Ferreira, M., Cardoso, A. P., & Abrantes, J. L. (2021). Motivation and relationship of the student with the school as factors involved in the perceived learning. Procedia - Social and Behavioral Sciences and Behavioral Sciences, 29, 1707-1714. https://doi.org/10.1016/j.sbspro.2021.07.089
García-Chitiva, M. P., & Suárez-Guerrero, C. (2019). Estado de la competencia digital docente en Latinoamérica. Revista de Comunicación, 18(2), 73-89. https://doi.org/10.26441/RC18.2-2019-A4
García-Peñalvo, F. J., Corell, A., Abella-García, V., & Grande-de-Prado, M. (2021). Recommendations for mandatory online assessment in higher education during the COVID-19 pandemic. Lecture Notes in Computer Science, 12749, 70-87. https://doi.org/10.1007/978-3-030-78270-2_5
Gonzalez-Ramirez, J., Mulqueen, K., Zealand, R., Silverstein, S., Mulqueen, C., & BuShell, S. (2022). Emergency online learning: College students' perceptions during the COVID-19 pandemic. College Student Journal, 56(1), 29-46.
Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. (2018). Predicting academic performance: A systematic literature review. Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, 175-199. https://doi.org/10.1145/3293881.3295783
Henderikx, M. A., Kreijns, K., & Kalz, M. (2019). To change or not to change? That's the question: On MOOC-success, barriers and their implications. Australasian Journal of Educational Technology, 35(3), 1-16. https://doi.org/10.14742/ajet.3942
Huang, X., Chandra, A., DePaolo, C. A., Cribbs, J., & Simmons, L. L. (2020). Measuring transactional distance in web-based learning environments: An initial instrument development. Open Learning: The Journal of Open, Distance and e-Learning, 35(3), 257-271. https://doi.org/10.1080/02680513.2015.1128366
Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2021). Student engagement predictions in an e-learning system and their impact on student course assessment scores. Computational Intelligence and Neuroscience, 2021, Article 6347792. https://doi.org/10.1155/2021/6347792
Ifenthaler, D., & Schumacher, C. (2020). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), 923-938. https://doi.org/10.1007/s11423-016-9477-y
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1961-1990. https://doi.org/10.1007/s11423-020-09788-z
Martin, F., Ritzhaupt, A., Kumar, S., & Budhrani, K. (2022). Award-winning faculty online teaching practices: Course design, assessment and evaluation, and facilitation. The Internet and Higher Education, 42, 100793. https://doi.org/10.1016/j.iheduc.2019.100793
Muljana, P. S., & Luo, T. (2019). Factors contributing to student retention in online learning and recommended strategies for improvement: A systematic literature review. Journal of Information Technology Education: Research, 18, 19-57. https://doi.org/10.28945/4182
Nuankaew, P., Nuankaew, W., Teeraputon, D., Phanniphong, K., & Bussaman, S. (2019). Prediction model of student achievement in massive open online courses. International Journal of Emerging Technologies in Learning, 14(18), 160-175. https://doi.org/10.3991/ijet.v14i18.10853
Pérez-López, E., Atochero, A. V., & Rivero, S. C. (2021). Educación a distancia en tiempos de COVID-19: Análisis desde la perspectiva de los estudiantes universitarios. RIED. Revista Iberoamericana de Educación a Distancia, 24(1), 331-350. https://doi.org/10.5944/ried.24.1.27855
Ramos-de-Robles, S. L., Arán-Jansen, P., & Ortega-Medellín, E. M. (2021). Variables asociadas al éxito académico en estudiantes de posgrado en línea. Revista Electrónica de Investigación Educativa, 23, e14. https://doi.org/10.24320/redie.2021.23.e14.3594
Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020). Analyzing and predicting students' performance by means of machine learning: A review. Applied Sciences, 10(3), 1042. https://doi.org/10.3390/app10031042
Rodríguez-Abitia, G., & Bribiesca-Correa, G. (2021). Assessing digital transformation in universities. Future Internet, 13(2), 52. https://doi.org/10.3390/fi13020052
Stone, C., & O'Shea, S. (2019). Older, online and first: Recommendations for retention and success. Australasian Journal of Educational Technology, 35(1), 57-69. https://doi.org/10.14742/ajet.3913
Tsai, Y. S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Kloos, C. D., & Gašević, D. (2020). Learning analytics in European higher education: Trends and barriers. Computers & Education, 155, 103933. https://doi.org/10.1016/j.compedu.2020.103933
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). Predictive analytics in education: A comparison of machine learning algorithms for student success. Journal of Educational Data Mining. Computers in Human Behavior, 89, 98-110. https://doi.org/10.1016/j.chb.2018.07.027
Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicción del rendimiento académico de los estudiantes a partir de los macrodatos de entornos virtuales de aprendizaje mediante modelos de aprendizaje. Computers in Human Behavior, 104, 106189. https://doi.org/10.1016/j.chb.2019.106189
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G. J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human-Computer Interaction, 35(4-5), 356-373. https://doi.org/10.1080/10447318.2018.1543084
Yağcı, M. (2022). Educational data mining: Prediction of students' academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 11. https://doi.org/10.1186/s40561-022-00192-z
Zambrano, J., Kirschner, F., Sweller, J., & Kirschner, P. A. (2021). Effects of group experience and information distribution on collaborative learning. Instructional Science, 47(5), 531-550. https://doi.org/10.1007/s11251-019-09495-0
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