Estudio comparativo de técnicas de analítica del aprendizaje para predecir el rendimiento académico de los estudiantes de educación superior
DOI:
https://doi.org/10.29059/cienciauat.v15i1.1392Palabras clave:
compromiso del estudiante, motivación del estudiante, desempeño académico, analítica del aprendizajeResumen
La deserción escolar involucra diversos factores, entre ellos, el compromiso del estudiante, a través del cual se puede predecir su éxito en la escuela. Ese compromiso tiene varios componentes, tales como conductual, emocional y cognitivo. La motivación y el compromiso están fuertemente relacionadas, ya que la primera es un precursor del compromiso. El objetivo de este estudio fue comparar la eficacia de la regresión lineal contra dos técnicas de minería de datos para predecir el rendimiento académico de los estudiantes en la educación superior. Se hizo un estudio transversal explicativo en el que se encuestó a 222 estudiantes universitarios de una institución pública de la Ciudad de México. Se realizó un análisis de regresión lineal jerárquico (RL) y de técnicas de analítica del aprendizaje, como redes neuronales (RN) y máquinas de vector soporte (SVM). Para evaluar la exactitud de las técnicas de analítica del aprendizaje se realizó un análisis de varianza (ANOVA). Se compararon las técnicas de analítica del aprendizaje y de regresión lineal usando la validación cruzada. Los resultados mostraron que el compromiso conductual y la autoeficacia tuvieron efectos positivos en el desempeño del estudiante, mientras que la pasividad mostró un efecto negativo. Asimismo, las técnicas de RL y de SVM pronosticaron igualmente el desempeño académico de los estudiantes. La RL tuvo la ventaja de producir un modelo simple y de fácil interpretación. Por el contrario, la técnica de SVM generó un modelo más complejo, aunque, si el modelo tuviese como objetivo el pronóstico del desempeño, la técnica SVM sería la más adecuada, ya que no requiere la verificación de ningún supuesto estadístico.
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Appleton, J. J., Christenson, S. L., and Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools. 45(5): 369-386.
Appleton, J. J., Christenson, S. L., Kim, D., and Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the Student Engagement Instrument. Journal of School Psychology. 44(5): 427-445.
Arnold, K. E. (2010). Signals: Applying Academic Analytics. EDUCAUSE Quarterly. 33(1): 1.
Caruth, G. D. (2018). Student Engagement, Retention, and Motivation: Assessing academic success in today’s college students (Vol. 5). Online Submission. 5(1): 17-30.
Chatzistamatiou, M., Dermitzaki, I., Efklides, A., and Leondari, A. (2015). Motivational and affective determinants of self-regulatory strategy use in elementary school mathematics. Educational Psychology. 35(7): 835-850.
Fernández, A. G., Paoloni, P. V., Rinaudo, M. C., and Donolo, D. (2013). Situational interest in Spanish language class on secondary education: Structural relations with engagement, disaffection, and performance. Universitas Psychologica. 12(3): 753-765.
Fredricks, J. A., Blumenfeld, P. C., and Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research. 74(1): 59-109.
Gasevic, D., Dawson, S., and Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends. 59(1): 64-71.
González, A., Rodríguez, Y., Faílde, J. M., and Carrera, M. V. (2016). Anxiety in the statistics class: Structural relations with self-concept, intrinsic value, and engagement in two samples of undergraduates. Learning and Individual Differences. 45: 214-221.
Halibas, A. S., Pillai, I. G., and Matthew, A. C. (2018). Utilization of RFID analytics in assessing student engagement. [En línea]. Disponible en: https://doi.org/10.1109/IT-DREPS.2017.8277809. Fecha de consulta: 8 de mayo de 2019.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. (2009). The WEKA data mining software: An update. SIGKDD Explor. Newsl. 11(1): 10-18.
King, R. B. and McInerney, D. M. (2016). Do goals lead to outcomes or can it be the other way around? Causal ordering of mastery goals, metacognitive strategies, and achievement. British Journal of Educational Psychology. 86(2): 296-312.
Li, Y. and Lerner, R. M. (2011). Trajectories of school engagement during adolescence: Implications for grades, depression, delinquency, and substance use. Developmental Psychology. 47(1): 233-247.
Liu, M., Calvo, R. A., and Pardo, A. (2013). Tracer: A tool to measure and visualize student engagement in writing activities. IEEE 13th International Conference on Advanced Learning Technologies. 421-425.
Martin, A. J. (2012). Part II Commentary: Motivation and engagement: Conceptual, operational, and empirical clarity. In S. L. Christenson, A. L. Reschly, and C. Wylie (Eds.), Handbook of Research on Student Engagement (pp. 303-311). Boston, MA: Springer.
Mayer, J. D., Chabot, H. F., and Carlsmith, K. M. (1997). Conation, affect, and cognition in personality. In G. Matthews (Ed.), Advances in Psychology (pp. 31-63). North-Holland, The Netherlands: Elsevier.
Meyers, L. S., Gamst, G., and Guarino A. J. (2006). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: SAGE Publications. 1016 Pp.
Moubayed, A., Injadat, M., Shami, A., and Lutfiyya, H. (2018). Relationship between student engagement and performance in e-learning environment using association rules, In 2018 IEEE World Engineering Education Conference (EDUNINE). [En línea]. Disponible en: https://doi.org/10.1109/EDUNINE.2018.8451005. Fecha de consulta: 29 de junio de 2019.
Newmann, F. M., Wehlage, G. G., and Lamborn, S. (1992). The Significance and sources of student engagement (In Student engagement and achievement in American secondary schools, ed. F.M. Newmann,). New York: Teachers College Press. 11-39 Pp.
Olivier, E., Archambault, I., and Dupéré, V. (2018). Boys’ and girls’ latent profiles of behavior and social adjustment in school: Longitudinal links with later student behavioral engagement and academic achievement? Journal of School Psychology. 69: 28-44.
Pintrich, P. R. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). [En línea]. Disponible en: https://eric.ed.gov/?id=ED338122. Fecha de consulta: 7 de febrero de 2018.
Pintrich, P. R. and de-Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology. 82(1): 33-40.
Ramirez-Arellano, A., Acosta-Gonzaga, E., Bory-Reyes, J., and Hernández-Simón, L. M. (2018). Factors affecting student learning performance: A causal model in higher blended education. Journal of Computer Assisted Learning. 34(6): 807-815.
Ramirez-Arellano, A., Bory-Reyes, J., and Hernández-Simón, L. M. (2019). Emotions, motivation, cognitive–metacognitive strategies, and behavior as predictors of learning performance in blended learning. Journal of Educational Computing Research. 57(2): 491-512.
Reschly, A. L. and Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evolution and future directions of the engagement construct. In S. L. Chris-tenson, A. L. Reschly, and C. Wylie (Eds.), Handbook of Research on Student Engagement (pp. 3-19). Boston, MA: Springer.
Reyes, N. S., Morales, J., Moya, J. G., Teran, C. E., Rodriguez, D. N. y Altamirano, G. C. (2019). Modelo para predecir el rendimiento académico basado en redes neuronales y analítica de aprendizaje. Revista Ibérica de Sistemas e Tecnologias de Informação. (E17): 258-266.
Salmela-Aro, K. and Upadyaya, K. (2014). School burnout and engagement in the context of demands–resources model. British Journal of Educational Psychology. 84(1): 137-151.
Siemens, G. and Gasevic, D. (2012). Guest editorial-learning and knowledge analytics. Journal of Educational Technology y Society: 15(3): 1-2.
Skinner, E. A. and Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology. 85(4): 571-581.
Skinner, E., Furrer, C., Marchand, G., and Kindermann, T. (2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic? Journal of Educational Psychology. 100(4): 765-781.
Skinner, E. A. and Pitzer, J. R. (2012). Developmental dynamics of student engagement, coping, and every-day resilience. In S. L. Christenson, A. L. Reschly, and C. Wylie (Eds.), Handbook of Research on Student Engagement (pp. 21-44). Boston, MA: Springer.
Sun, Z., Xie, K., and Anderman, L. H. (2018). The role of self-regulated learning in students’ success in flipped undergraduate math courses. The Internet and Higher Education. 36: 41-53.
Symonds, J., Schoon, I., and Salmela-Aro, K. (2016). Developmental trajectories of emotional disengagement from schoolwork and their longitudinal associations in England. British Educational Research Journal. 42(6): 993-1022.
Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education. 48(6): 1273-1296.
Tanuar, E., Heryadi, Y., Lukas, Abbas, B., S., and Gaol, F. L. (2018). Using machine learning techniques to earlier predict student’s performance. 2018 Indonesian Association for Pattern Recognition International Conference (INAPR). 85-89.
Urbina-Nájera, A. B. y Mora, J. de la C. (2017). Breve revisión de aplicaciones educativas utilizando Minería de Datos y Aprendizaje Automático. Revista Electrónica de Investigación Educativa. 19(4): 84-96.
Vytasek, J. M., Patzak, A., and Winne, P. H. (2020). Analytics for student engagement. In M. Virvou, E. Alepis, G. A. Tsihrintzis, and L. C. Jain (Eds.), Machine Learning Paradigms: Advances in Learning Analytics (pp. 23-48). Switzerland: Springer Nature.
Wang, F. H. (2017). An exploration of online behaviour engagement and achievement in flipped classroom supported by learning management system. Computers y Education. 114: 79-91.
Wang, M. T. and Eccles, J. S. (2013). School context, achievement motivation, and academic engagement: A longitudinal study of school engagement using a multidimensional perspective. Learning and Instruction. 28: 12-23.
Wong, A. and Chong, S. (2018). Modelling adult learners’ online engagement behaviour: Proxy measures and its application. Journal of Computers in Education. 5(4): 463-479.
Yadav, S. K., Bharadwaj, B., and Pal, S. (2012). Data mining applications: A comparative study for predicting student’s performance, in ArXiv: 1202.4815 [Cs]. [En línea]. Disponible en: http://arxiv.org/abs/1202.4815. Fecha de consulta: 7 de abril de 2019.
