Modelo predictivo de las metas de aproximación al dominio: un análisis con IA

Francisco Quiñonez Tapia; María de Lourdes Vargas Garduño

doi:10.24320/redie.2026.28.e10.6555

Autores

Francisco Quiñonez Tapia Universidad de Guadalajara https://orcid.org/0000-0002-7371-0690
María de Lourdes Vargas Garduño Universidad Michoacana de San Nicolás de Hidalgo https://orcid.org/0000-0002-7384-3098

DOI:

https://doi.org/10.24320/redie.2026.28.e10.6555

Palabras clave:

objetivo educativo, voluntad de logro, comunicación intercultural, psicología educativa

Resumen

El objetivo del estudio fue predecir con inteligencia artificial las Metas de Aproximación al Dominio en estudiantes de secundaria a partir de las variables recuperadas de la base de datos de PISA en 2018. La muestra quedó integrada por 339,560 participantes de 60 países. Metodológicamente, se utilizó el aprendizaje automático con el meta-algoritmo Super Learner con 14 algoritmos candidatos; asimismo, se utilizó el aprendizaje profundo con una red neuronal de 107 parámetros. Los análisis arrojaron que el Apoyo emocional de los padres percibido por el estudiante, la Competitividad, Sentido de vida, Autoeficacia y Conocimiento de la comunicación intercultural fueron predictores de las Metas de Aproximación al Dominio en los estudiantes de secundaria. La investigación contribuye al avance del estudio de los factores que influyen en las metas de aproximación del dominio en estudiantes de secundaria, con modelos de análisis que permiten avanzar en el desarrollo de la ciencia de la educación predictiva con inteligencia artificial.

Descargas

Los datos de descarga aún no están disponibles.

Referencias

Albreiki, B., Zaki, N. y Alashwal, H. (2021). A Systematic literature review of student’ performance prediction using machine learning techniques. Education Sciences, 11(9), 552. https://doi.org/10.3390/educsci11090552

Alhadabi, A. y Karpinski, A. C. (2020). Grit, self-efficacy, achievement orientation goals, and academic performance in University students. International Journal of Adolescence and Youth, 25(1), 519-535. https://doi.org/10.1080/02673843.2019.1679202

Ariani, D. W. (2022). How achievement goals affect students’ well-being and the relationship model between achievement goals, academic self-efficacy and affect at school. Journal of Applied Research in Higher Education, 14(1), 111-134. https://doi.org/10.1108/jarhe-08-2020-0273

Babenko, O. y Oswald, A. (2019). The roles of basic psychological needs, self-compassion, and self-efficacy in the development of mastery goals among medical students. Medical Teacher, 41(4), 478-481. https://doi.org/10.1080/0142159X.2018.1442564

Bandura, A. (1978). Self-efficacy: Toward a unifying theory of behavioral change. Advances in Behaviour Research and Therapy, 1(4), 139-161. https://doi.org/10.1016/0146-6402(78)90002-4

Danthony, S., Mascret, N. y Cury, F. (2021). The relationships between the 3 x 2 achievement goal model and test anxiety in Physical Education. European Physical Education Review, 27(3), 559-573. https://doi.org/10.1177/1356336X20971325

Datu, J. A. D., Valdez, J. P. M. y Yang, W. (2022). La vida comprometida académicamente de los estudiantes orientados al dominio: orden causal entre emociones positivas, metas de dominio y compromiso académico. Revista de Psicodidáctica, 27(1), 1-8. https://doi.org/10.1016/j.psicod.2021.02.001

Dignum, V. (2019). What is artificial intelligence? En Responsible artificial intelligence: How to develop and use AI in a responsible way (pp. 9-34). Springer. https://doi.org/10.1007/978-3-030-30371-6_2

Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040-1048. https://doi.org/10.1037/0003-066X.41.10.1040

Elliot, A. J. (1999). Approach and avoidance motivation and achievement goals. Educational Psychologist, 34, 169-189. https://doi.org/10.1207/s15326985ep3403_3

Elliot, A. J. (2006). The hierarchical model of approach-avoidance motivation. Motivation and Emotion, 30(2), 111-116. https://doi.org/10.1007/s11031-006-9028-7

Elliot, A. J. y Harackiewicz, J. M. (1996). Approach and avoidance achievement goals and intrinsic motivation: A mediational analysis. Journal of Personality and Social Psychology, 70(3), 461-475. https://doi.org/10.1037/0022-3514.70.3.461

Elliot, A. J. y McGregor, H. A. (2001). A 2 X 2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501-519. https://doi.org/10.1037/0022-3514.80.3.501

Elliot, A. J., Murayama, K. y Pekrun, R. (2011). A 3 × 2 achievement goal model. Journal of Educational Psychology, 103(3), 632-648. https://doi.org/10.1037/a0023952

Elliot, A. J. y Thrash, T. M. (2001). Achievement Goals and the Hierarchical Model of Achievement Motivation. Educational Psychology Review, 13, 139-156. https://doi.org/10.1023/A:1009057102306

Golmakani, M. K. y Polley, E. C. (2020). Super learner for survival data prediction. The International Journal of Biostatistics, 16(2), 20190065. https://doi.org/10.1515/ijb-2019-0065

Guo, J., Hu, X., Elliot, A. J., Marsh, H. W., Murayama, K., Basarkod, G., Parker, P. D. y Dicke, T. (2023). Mastery-approach goals: A large-scale cross-cultural analysis of antecedents and consequences. Journal of Personality and Social Psychology, 125(2), 397-420. https://doi.org/10.1037/pspp0000436

Harackiewicz, J. M., Barron, K. E., Carter, S. M., Lehto, A. T. y Elliot, A. J. (1997). Predictors and consequences of achievement goals in the college classroom: Maintaining interest and making the grade. Journal of Personality and Social Psychology, 73(6), 1284-1295. https://doi.org/10.1037/0022-3514.73.6.1284

Hastie, T., Tibshirani, R. y Friedman, J. (2009). The elements of statistical learning. Springer. https://doi.org/10.1007/978-0-387-84858-7

Hilbert, S., Coors, S., Kraus, E., Bischl, B., Lindl, A., Frei, M., Wild, J., Krauss, S., Goretzko, D. y Stachl, C. (2021). Machine learning for the educational sciences. Review of Education, 9(3), e3310. https://doi.org/10.1002/rev3.3310

Jaitner, D., Rinas, R., Becker, C., Niermann, C., Breithecker, J. y Mess, F. (2019). Supporting subject justification by educational psychology: A systematic review of achievement goal motivation in school physical education. Frontiers in Education, 4. https://www.frontiersin.org/articles/10.3389/feduc.2019.00070

Janiesch, C., Zschech, P. y Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31, 685-695. https://doi.org/10.1007/s12525-021-00475-2

Jiang, H. (2021). Machine learning fundamentals: A concise introduction. Cambridge University Press. https://doi.org/10.1017/9781108938051

Jung, A. (2022). Machine learning: The basics. Springer Nature. https://doi.org/10.1007/978-981-16-8193-6

Kingma, D. P. y Ba, J. (2015). Adam: A method for stochastic optimization. 1-15. http://arxiv.org/abs/1412.6980

Li, Q., Zhao, J., Tian, J., Sun, T., Zhao, C., Guo, H., Zhu, L., Gao, R., Yang, L., Cao, D. y Zhang, S. (2021). The association among achievement goal orientations, academic performance, and academic well-being among chinese medical students: A cross-sectional study. Frontiers in Psychology, 12. https://www.frontiersin.org/articles/10.3389/fpsyg.2021.694019

Luan, H. y Tsai, C. C. (2021). A review of using machine learning approaches for precision education. Educational Technology y Society, 24(1), 250-266. JSTOR.

Lyre, H. (2020). The state space of Artificial intelligence. Minds and Machines, 30(3), 325-347. https://doi.org/10.1007/s11023-020-09538-3

Montesinos, O. A., Montesinos, A. y Crossa, J. (2022). Multivariate statistical machine learning methods for genomic prediction. Springer. https://doi.org/10.1007/978-3-030-89010-0

OECD. (2018). Student questionnaire data file [Data file]. https://webfs.oecd.org/pisa2018/SPSS_STU_QQQ.zip

OECD. (2019). PISA 2018 Results (Volume III). What school life means for students’ lives. https://doi.org/10.1787/acd78851-en

OECD. (2020). PISA 2018 Results (Volume VI). Are students ready to thrive in an interconnected world? https://doi.org/10.1787/d5f68679-en

Quiñonez-Tapia, F. y Vargas-Garduño, M. de L. (2023). Entusiasmo del profesorado, Instrucción Adaptativa y Metas de Aproximación al Dominio desde el punto de vista de estudiantes de Secundaria. Revista de Investigación en Educación, 21(3), 451-465. https://doi.org/10.35869/reined.v21i3.4981

Rickert, N. P. y Skinner, E. A. (2022). Parent and teacher involvement and the development of students’ academic engagement: A growth curve analysis over four time points. Journal of Adolescence, 94(2), 224-239. https://doi.org/10.1002/jad.12019

Spence, J. T. y Helmreich, R. L. (1983). Achievement-related motives and behavior. En Achievement and achievement motives: Psychological and sociological approaches (pp. 10-74). Freeman.

Steger, M. F. (2009). Meaning in Life. En S. J. Lopez y C. R. Snyder (Eds.), The Oxford handbook of positive psychology (pp. 679-689). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195187243.013.0064

Steger, M. F. y Dik, B. J. (2009). If one is looking for meaning in life, does it help to find meaning in work? Applied Psychology: Health and Well-Being, 1(3), 303-320. https://doi.org/10.1111/j.1758-0854.2009.01018.x

Torra, V., Karlsson, A., Steinhauer, H. J. y Berglund, S. (2019). Artificial Intelligence. En A. Said y V. Torra (Eds.), Data science in practice (pp. 9-26). Springer. https://doi.org/10.1007/978-3-319-97556-6_2

van der Laan, M. J., Polley, E. C. y Hubbard, A. E. (2007). Super learner. 6(1). https://doi.org/10.2202/1544-6115.1309

Webb, M. E., Fluck, A., Magenheim, J., Malyn-Smith, J., Waters, J., Deschênes, M. y Zagami, J. (2021). Machine learning for human learners: Opportunities, issues, tensions and threats. Educational Technology Research and Development, 69(4), 2109-2130. https://doi.org/10.1007/s11423-020-09858-2