Metacognitive knowledge and skills in students with deep approach to learning. Evidence from mathematical problem solving

  1. Trinidad García 1
  2. Marisol Cueli 1
  3. Celestino Rodríguez 1
  4. Jennifer Krawec 2
  5. Paloma González Castro 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 University of Miami
    info

    University of Miami

    Coral Gables, Estados Unidos

    ROR https://ror.org/02dgjyy92

Revista:
Revista de psicodidáctica

ISSN: 1136-1034

Año de publicación: 2015

Volumen: 20

Número: 2

Páginas: 209-226

Tipo: Artículo

DOI: 10.1387/REVPSICODIDACT.13060 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista de psicodidáctica

Objetivos de desarrollo sostenible

Resumen

Student approaches to learning and metacognitive strategies are two important conditioning factors in solving mathematical problems. The evidence suggests that it is the deep approach to learning which leads to student success in such tasks. The present study focused on analyzing the differences in metacognitive knowledge and skills in a sample of 524 fifth and sixth grade students divided into three groups based on their different levels of use of a deep a pproach (241 = low; 152 = medium; and 131 = high). Metacognitive knowledge was assessed using the Learning Strategies Knowledge Questionnaire, while evidence about metacognitive skills was gathered by means of process measures (Triple Tasks Procedure) during students’ solving of two mathematical word problems. Statistically significant differences in metacognitive knowledge were found among groups while differences in metacognitive skills were only found in the second task, with a low effect size.

Referencias bibliográficas

  • Baeten, M., Kyndt, E., Struyven, K., & Dochy, F. (2010). Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness. Educational Research Review, 5(3), 243-260, doi: 10.1016/j.edurev. 2010.06.001
  • Biggs, J. (1987). Student approaches to learning and studying hawthorn. Australia: Australian Council for Educational Research.
  • Biggs, J. (1993). What do inventories of students’ learning processes really measure? A theoretical review and clarification. British Journal of Educational Psychology, 63(1), 3-19. doi: 10.1111/j.2044-8279.1993. tb01038.x
  • Biggs, J., Kember, D., & Leung, D. Y. P. (2001). The revised two-factor study process questionnaire: R-SPQ- 2F. British Journal of Educational Psychology, 71(1), 133-149. doi: 10.1348/000709901158433
  • Branch, J. L. (2000). Investigating the information-seeking processes of adolescents: The value of using thinkalouds and think afters. Library and Information Science Research, 22(4), 371-392.
  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. Washington: National Academies Press.
  • Bransford, J. D., & Stein, B. S. (1993). The ideal problem solver: A guide for improving thinking, learning and creativity (2nd ed.). New York: W.H. Freeman.
  • Cano, F., García, A., Justicia, F., & García-Berbén, A. B. (2014). Enfoques de aprendizaje y comprensión lectora: El papel de las preguntas de los estudiantes y del conocimiento previo. Revista de Psicodidáctica, 19(2), 247-265. doi: 10.1387/RevPsicodidact.10186
  • Chen, L. G., & McNamee, G. (2011). Positive approaches to learning in the context of Preschool classrooms activities. Early Childhood Education Journal, 39(1), 71-78. doi: 10.1007. s10643-010-0441-x
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New Jersey: Lawrence E rlbaum.
  • Finney, S. J., & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock, & R. O. Muller (Eds.), Structural equation modeling: A second course (pp. 269-314). Greenwich, CT: Information Age.
  • García, J. N., & Rodríguez, C. (2007). Influence of the recording interval and a graphic organizer on the writing process/product and on other psychological variables. Psicothema, 19(2), 198-205.
  • García, T., Betts, L. M., González-Castro, P., González-Pienda, J. A., & Rodríguez, C. (in press). On-line assessment of the process involved in Maths problem-solving in fifth and sixth grade students: Self-regulation and achievement. Revista Latinoamericana de Investigación en Matemática Educativa.
  • Harskamp, E., & Suhre, C. (2006). Improving mathematical problem solving: A computerized approach. Computers in Human Behavior, 22(5), 801-815. doi: 10.1016/j. chb.2004.03.023
  • Ifenthaler, D. (2012). Determining the effectiveness of prompts for self-regulated learning in problem-solving scenarios. Educational Technology & Society, 15(1), 38-52.
  • Kellog, R. T. (1987). Writing performance: Effects of cognitive strategies. Written Communication, 4(3), 269298.
  • Kizilgunes, B., Tekkaya, C., & Sungur, S. (2009). Modeling the relations among students’ epistemological beliefs, motivation, learning approach, and achievement. The Journal of Educational Research, 102(4), 243-255. doi: 10.3200/JOER.102.4.243-256
  • Krawec, J., Huang, J., Montague, M., Kressler, B., & Melia, A. (2012). The effects of cognitive strategy instruction on knowledge of math problems solving processes of middle school students with learning disabilities. Learning Disability Quarterly, 36(2), 80-92. doi: 10.1177/0731948712463368
  • Lucangeli, D., & Cabrele, S. (2006). The relationship of metacognitive knowledge, skills and beliefs in children with and without mathematical learning disabilities. In A. Desoete, & M. V. Veenman (Eds.), Metacognition in mathematics education (pp. 103-133). New York: Nova Science Publishers, Inc.
  • Macbeth, G., Razumiejczyk, E., & Ledesma, R. D. (2011). Cliff’s delta calculator: A non-parametric effect size program for two groups of observations. Universitas Psychologica, 10(2), 545-555.
  • Malmberg, J., Jarvenoja, H., & Jarvela, S. (2013). Patterns in elementary school students’ strategic actions in varying learning situations. Instructional Science, 41(5), 933-954. doi: 10.1007/s11251-012-9262-1
  • McInerney, D. M., Cheng, R. W., Mok, M. M. C., & Lam, A. K. H. (2012). Academic self-concept and learning strategies: Direction of effect on student academic achievement. Journal of Advanced Academics, 23(3), 249-269. doi: 10.1177/1932202X12451020
  • Montague, M., Enders, C., & Dietz, S. (2011). Effects of cognitive strategy instruction on math problem solving of middle school students with learning disabilities. Learning Disability Quarterly, 34(4), 262-272. doi: 10.1177/0731948711421762
  • Murayama, K., Pekrun, R., Lichtenfeld, S., & Vom Hofe, R. (2013). Predicting long-term growth in students’ mathematics achievement: The unique contributions of motivation and cognitive strategies. Child Development, 84(4), 1475-1490. doi: 10.1111/cdev.12036
  • Núñez, J. C., Cerezo, R., Bernardo, A., Rosário, P., Valle, A., Fernández,
  • E., & Suárez, N. (2011). Implementation of training programs in selfregulated learning strategies in moodle format: Results of an experience in higher education. Psicothema, 23(2), 274-281.
  • Özcan, C. Z. (2014). Assessment of metacognition in mathematics: Which one of two methods is a better predictor of mathematics achievement? International Online Journal of Educational Sciences, 6(1), 49-57. doi: 10.15345/iojes.2014.01.006
  • Pennequin, V., Sorel, O., Nanty, I., & Fontaine, R. (2010). Metacognition and low achievement in mathematics: The effect of training in the use of metacognitive skills to solve mathematical word problems. Thinking & Reasoning, 16(3), 198-220. doi: 10.1080/13546783.2010.509052
  • Piolat, A., Kellogg, R. T., & Farioli, F. (2001). The triple task technique for studying writing processes: On which task is attention focused? Current Psychology Letters: Brain, Behavior and Cognition, 4, 67-83.
  • Ranellucci, J., Muis, K. R., Duffy, M., Wang, X., Sampasivam, L., & Franco, G. (2013). To master or perform? Exploring relations between achievement goals and conceptual change learning. British Journal of Educational Psychology, 83(3), 431-451. doi: 10.1111/j.20448279.2012.02072.x
  • Rosário, P., Núñez, J. C., Ferrando, P., Paiva, O., Lourenço, A., Cerezo, R., &
  • Valle, A. (2013). The relationship between approaches to teaching and approaches to studying: A two-level structural equation model for biology achievement in high school. Metacognition and Learning, 8(1), 44-77. doi: 10.1007/s11409-013-9095-6
  • Sengodan, V., & Zanaton, Z. H. (2012). Students’ learning styles and intrinsic motivation in learning mathematics. Asian Social Science, 8(16), 1723. doi: 10.5539/ass.v8n16p17
  • Valle, A., Rodríguez, S., Cabanach, R. G., Núñez, J. C., González-Pienda, J. A., & Rosário, P. (2009). Diferencias en rendimiento académico según los niveles de las estrategias cognitivas y de las estrategias de autorregulación. Summa Psicológica UST, 6(2), 31-42.
  • Veenman, M. V. J. (2011). Learning to self-monitor and self-regulate. In R. Mayer, & P. Alexander (Eds.), Handbook of research on learning and instruction (pp. 197-218). New York: Routledge.
  • Whimbey, A., & Lochhead, J. (1993). Comprender y resolver problemas. Madrid: Visor.
  • Williams, J. R. (2008). Revising the Declaration of Helsinki. World Medical Journal, 54, 120-125.
  • Zimmerman, B. J. (2000). Attaining self-regulation. A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). San Diego, CA: Academic Press.
Fuente de los datos: Dialnet