Barra lateral de artículos

Enviado
March 29, 2023
Aceptado
October 27, 2023
Publicado
July 1, 2024
Descargar
PDF PDF (English)
Estadística
Leer contador : 610 Descargar : 447

Descargas

Los datos de descargas todavía no están disponibles.

Contenido del artículo principal

Resumen

El estudio de las relaciones entre la metacognición, las estrategias de aprendizaje y las emociones, tanto positivas como negativas, es una línea emergente de investigación que ha sido escasamente explorada. En consecuencia, el objetivo de este trabajo es analizar la relación entre estas variables mencionadas en 1096 estudiantes universitarios pertenecientes a diferentes programas académicos de una institución de educación superior colombiana. Para lo anterior, se realizó un análisis factorial por componentes principales para la reducción de dimensiones representadas en los ítems de los instrumentos usados y se aplicó el modelado de ecuaciones estructurales (SEM) para explicar las interrelaciones existentes entre las tres variables indagadas. Los resultados demuestran que hay una relación positiva entre la metacognición y las estrategias de aprendizaje, y estas, a su vez, con las emociones académicas positivas. Por el contrario, las emociones académicas negativas tienen una relación negativa con las estrategias de aprendizaje y la metacognición, lo que nos lleva a concluir que la metacognición fomenta las estrategias de aprendizaje y las emociones académicas negativas las desalientan.

Palabras clave

metacognición estrategias de aprendizaje perspectiva de aprendizaje educación universitaria percepciones de los estudiantes emociones

Detalles del artículo

Cómo citar
Briceño-Martinez, J. J., Barrios-Aguirre, F., & Castellanos-Saavedra, M. P. (2024). Relaciones entre la metacognición, las estrategias de aprendizaje y las emociones en estudiantes universitarios. PUBLICACIONES, 54(1), 235–280. https://doi.org/10.30827/publicaciones.v54i1.27736
Descargar cita
Endnote/Zotero/Mendeley (RIS)
BibTeX
Crossref
Scopus
Google Scholar
Europe PMC

Referencias

  1. Acosta-Gonzaga, E., & Ramirez-Arellano, A. (2021). The Influence of Motivation, Emotions, Cognition, and Metacognition on Students’ Learning Performance: A Comparative Study in Higher Education in Blended and Traditional Contexts. SAGE Open, 11(2). https://doi.org/10.1177/21582440211027561
  2. Aizpurua, A., Lizaso, I., & Iturbe, I. (2018). Estrategias de aprendizaje y habilidades de razonamiento de estudiantes universitarios. Revista de Psicodidáctica, 23(2), 110–116. https://doi.org/https://doi.org/10.1016/j.psicod.2018.01.001
  3. Artino, A. R., & Jones, K. D. (2012). Exploring the complex relations between achievement emotions and self-regulated learning behaviors in online learning. Internet and Higher Education, 15(3), 170–175. https://doi.org/10.1016/j.iheduc.2012.01.006.
  4. Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-Regulated Learning: Beliefs, Techniques, and Illusions. Annual Review of Psychology, 64(1), 417–444. https://doi.org/10.1146/annurev-psych-113011-143823
  5. Brady, A. C., Kim, Y. E., & Cutshall, J. (2021). The what, why, and how of distractions from a self-regulated learning perspective. Journal of college reading and learning, 51(2), 153–172. https://doi-org.ezproxy.uan.edu.co/10.1080/10790195.2020.1867671
  6. Bol, L., & Hacker, D. (2012). Calibration Research: Where Do We Go from Here? Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00229
  7. Broadbent, J. (2017). Comparing online and blended learner’s self-regulated learning strategies and academic performance. The Internet and Higher Education, 33, 24–32. https://doi.org/https://doi.org/10.1016/j.iheduc.2017.01.004
  8. Byrne, B. M. (2013). Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming. Structural Equation Modeling with Mplus. https://doi.org/10.4324/9780203807644
  9. Celik, B. (2022). The Effect of Metacognitive Strategies on Self-Efficacy, Motivation and Academic Achievement of University Students. Canadian Journal of Educational and Social Studies, 2(4), 37–55.
  10. Cervin-Ellqvist, M., Larsson, D., Adawi, T., Stöhr, C., & Negretti, R. (2021). Metacognitive illusion or self-regulated learning? Assessing engineering students’ learning strategies against the backdrop of recent advances in cognitive science. Higher Education, 82(3), 477–498. https://doi.org/10.1007/s10734-020-00635-x
  11. Chang, C., Colón-Berlingeri, M., Mavis, B., Laird-Fick, H. S., Parker, C., & Solomon, D. (2021). Medical student progress examination performance and its relationship with metacognition, critical thinking, and self-regulated learning strategies. Academic Medicine, 96(2), 278–284.
  12. Chin, E. C. H., Williams, M. W., Taylor, J. E., & Harvey, S. T. (2017). The influence of negative affect on test anxiety and academic performance: An examination of the tripartite model of emotions. Learning and Individual Differences, 54, 1–8. https://doi.org/https://doi.org/10.1016/j.lindif.2017.01.002
  13. Connor, C. M., Day, S. L., Zargar, E., Wood, T. S., Taylor, K. S., Jones, M. R., & Hwang, J. K. (2019). Building word knowledge, learning strategies, and metacognition with the Word-Knowledge e-Book. Computers & Education, 128, 284–311. https://doi.org/https://doi.org/10.1016/j.compedu.2018.09.016
  14. Ebomoyi, J. I. (2020). Metacognition and Peer Learning Strategies as Predictors in Problem-Solving Performance in Microbiology. Journal of Microbiology & Biology Education, 21(1), 10. https://doi.org/10.1128/jmbe.v21i1.1715
  15. Efklides, A. (2011). Interactions of Metacognition With Motivation and Affect in Self-Regulated Learning: The MASRL Model. Educational Psychologist, 46(1), 6–25. https://doi.org/10.1080/00461520.2011.538645
  16. Ekatushabe, M., Nsanganwimana, F., Muwonge, C. M., & Ssenyonga, J. (2021). The relationship between cognitive activation, self-efficacy, achievement emotions and (meta) cognitive learning strategies among Ugandan biology learners. African journal of research in mathematics, science and technology education, 25(3), 247-258. https://doi-org.ezproxy.uan.edu.co/10.1080/18117295.2021.2018867
  17. Erbas, A. K., & Okur, S. (2012). Researching students? strategies, episodes, and metacognitions in mathematical problem solving. Quality & Quantity, 46(1), 89–102.
  18. Flavell, J. H. (1976). Metacognitive aspects of problem solving. The Nature of Intelligence, 231–235. https://doi.org/10.12691/education-4-2-5
  19. Gargallo, B., Jesús, S.-R., & Pérez-Pérez, C. (2009). El cuestionario CEVEAPEU. Un instrumento para la evaluación de las estrategias de aprendizaje de los estudiantes universitarios. RELIEVE. Revista Electrónica de Investigación y Evaluación Educativa, 15(2), 1–31.
  20. González, A., Fernández, M.-V. C., & Paoloni, P.-V. (2017). Hope and anxiety in physics class: Exploring their motivational antecedents and influence on metacognition and performance. Journal of Research in Science Teaching, 54(5), 558–585. https://doi.org/https://doi.org/10.1002/tea.21377
  21. Guterman, O., & Neuman, A. (2022). Not all paths lead to success: learning strategies and achievement among undergraduate students. Journal of Further and Higher Education, 46(1), 115-127. https://doi-org.ezproxy.uan.edu.co/10.1080/0309877X.2021.1890701
  22. Hayat, A. A., Shateri, K., Amini, M., & Shokrpour, N. (2020). Relationships between academic self-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: a structural equation model. BMC Medical Education, 20(1), 76. https://doi.org/10.1186/s12909-020-01995-9
  23. Hertel, S., & Karlen, Y. (2021). Implicit theories of self-regulated learning: Interplay with students’ achievement goals, learning strategies, and metacognition. British Journal of Educational Psychology, 91(3), 972–996. https://doi.org/10.1111/bjep.12402
  24. Huertas Bustos, A. P., Vesga Bravo, G. J., & Galindo León, M. (2014). Validación del instrumento’Inventario de habilidades metacognitivas (mai)’con estudiantes colombianos. Praxis & Saber, 5(10), 56–74.
  25. Karlen, Y., Hirt, C. N., Liska, A., & Stebner, F. (2021). Mindsets and Self-Concepts About Self-Regulated Learning: Their Relationships With Emotions, Strategy Knowledge, and Academic Achievement. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.661142
  26. Magno, C. (2010). The role of metacognitive skills in developing critical thinking. Metacognition and Learning, 5(2), 137–156.
  27. McDaniel, M. A., & Einstein, G. O. (2020). Training Learning Strategies to Promote Self-Regulation and Transfer: The Knowledge, Belief, Commitment, and Planning Framework. Perspectives on Psychological Science, 15(6), 1363–1381. https://doi.org/10.1177/1745691620920723
  28. McDaniel, M. A., Einstein, G. O., & Een, E. (2021). Training College Students to Use Learning Strategies: A Framework and Pilot Course. Psychology Learning & Teaching, 20(3), 364–382. https://doi.org/10.1177/1475725721989489
  29. Ochoa Sierra, L., & Moya Pardo, C. (2019). La evaluación docente universitaria: retos y posibilidades. Folios, (49), 41-60. https://doi.org/10.17227/folios.49-9390
  30. Pekrun, R. (2021). Teachers need more than knowledge: Why motivation, emotion, and self-regulation are indispensable. Educational Psychologist, 56(4), 312–322. https://doi-org.ezproxy.uan.edu.co/10.1080/00461520.2021.1991356
  31. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. https://doi.org/https://doi.org/10.1016/j.cedpsych.2010.10.002
  32. Pekrun, R., Goetz, T., & Perry, R. P. (2005). Achievement emotions questionnaire (AEQ). User’s manual. Unpublished Manuscript, University of Munich.
  33. Pintrich, P. R., Wolters, C. A., & Baxter, G. P. (2000). assessing metacognition and self-regulated learning. En G. Gregory & C. James (Ed.), Issues in the Measurement of Metacognition (pp. 43–97). Buros Institute of Mental Measurements.
  34. Price, M. J., Mudrick, N. V, Taub, M., & Azevedo, R. (2018). The Role of Negative Emotions and Emotion Regulation on Self-Regulated Learning with MetaTutor. En R. Nkambou, R. Azevedo, & J. Vassileva (Eds.), Intelligent Tutoring Systems (pp. 170–179). Springer International Publishing.
  35. Ramirez-Arellano, A., Acosta-Gonzaga, E., Bory-Reyes, J., & 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. https://doi.org/10.1111/jcal.12289
  36. Ramirez-Arellano, A., Bory-Reyes, J., & 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. https://doi.org/10.1177/0735633117753935
  37. Rhodes, M. G. (2019). Metacognition. Teaching of Psychology, 46(2), 168–175. https://doi.org/10.1177/0098628319834381
  38. Roberts, J. S. (2021). Integrating Metacognitive Regulation into the Online Classroom Using Student-Developed Learning Plans. Journal of Microbiology & Biology Education, 22(1), ev22i1.2409. https://doi.org/10.1128/jmbe.v22i1.2409
  39. Sáiz-Manzanares, M. C., & Montero-García, E. (2015). Metacognition, Self-regulation and Assessment in Problem-Solving Processes at University. En A. Peña-Ayala (Ed.), Metacognition: Fundaments, Applications, and Trends: A Profile of the Current State-Of-The-Art (pp. 107–133). Springer International Publishing. https://doi.org/10.1007/978-3-319-11062-2_5
  40. Samuelowicz, K., & Bain, J. D. (2001). Revisiting academics’ beliefs about teaching and learning. Higher Education, 41(3), 299–325.
  41. Sánchez-Rosas, J. (2015). The Achievement Emotions Questionnaire-Argentine (AEQ-AR): internal and external validity, reliability, gender differences and norm-referenced interpretation of test scores. Revista Evaluar, 15(1 SE-Investigaciones originales). https://doi.org/10.35670/1667-4545.v15.n1.14908
  42. Schraw, G., & Dennison, R. S. (1994). Assessing Metacognitive Awareness. Contemporary Educational Psychology, 19(4), 460–475. https://doi.org/https://doi.org/10.1006/ceps.1994.1033
  43. Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7(4), 351–371. https://doi.org/10.1007/BF02212307
  44. Tsai, C. W., Lee, L. Y., Cheng, Y. P., Lin, C. H., Hung, M. L., & Lin, J. W. (2022). Integrating online meta-cognitive learning strategy and team regulation to develop students’ programming skills, academic motivation, and refusal self-efficacy of Internet use in a cloud classroom. Universal Access in the Information Society, 1-16. https://doi.org/10.1007/s10209-022-00958-9
  45. Versteeg, M., Bressers, G., Wijnen-Meijer, M., Ommering, B. W. C., de Beaufort, A. J., & Steendijk, P. (2021). What Were You Thinking? Medical Students’ Metacognition and Perceptions of Self-Regulated Learning. Teaching and Learning in Medicine, 33(5), 473–482. https://doi.org/10.1080/10401334.2021.1889559
  46. Vrugt, A., & Oort, F. J. (2008). Metacognition, achievement goals, study strategies and academic achievement: pathways to achievement. Metacognition and Learning, 3(2), 123–146. https://doi.org/10.1007/s11409-008-9022-4
  47. Wilson, A. (2021). Towards an understanding of metacognition(ing) through an agential realism framework. Educational Philosophy and Theory, 1–14. https://doi.org/10.1080/00131857.2021.1915763
  48. Wittmann, S. (2011). Learning strategies and learning-related emotions among teacher trainees. Teaching and Teacher Education, 27(3), 524–532. https://doi.org/10.1016/j.tate.2010.10.006
  49. Zhao, N., Teng, X., Li, W., Li, Y., Wang, S., Wen, H., & Yi, M. (2019). A path model for metacognition and its relation to problem-solving strategies and achievement for different tasks. ZDM, 51(4), 641–653.