pp 1–11 | Cite as

Metacognition and motivation in school-aged children with and without mathematical learning disabilities in Flanders

  • Elke Baten
  • Annemie DesoeteEmail author
Original Article


The role of metacognitive postdiction accuracy and autonomous and controlled motivation in mathematics was explored in elementary school children (n = 208) within two perspectives, related to sample characteristics. A first study was set up in a population-based cohort. A second study was set up with children with and without a documented mathematical disability. Both studies revealed a concurrent relation between the metacognitive postdiction skills of children and their mathematical accuracy and speed, leading to the practical recommendation that teachers should pay attention to the accuracy of self-judgments of children. In addition, controlled motivation was negatively related to the speed and accuracy in study 2. Children with mathematical learning disabilities (MLD) differed from peers without mathematical learning disabilities on postdiction accuracy and autonomous motivation. However, they did not differ significantly on controlled motivation, suggesting the importance of differentiating between controlled and autonomous motivation when analyzing motivation in mathematics education.


Calculation accuracy Fact retrieval speed Metacognitive postdiction accuracy Self-judgment Autonomous motivation Controlled motivation Mathematical learning disabilities 


  1. Ackerman, P. L., & Ellingsen, V. J. (2016). Speed and accuracy indicators of test performance under different instructional conditions: Intelligence correlates. Intelligence, 56, 1–9. Scholar
  2. Azevedo, R. (2009). Theoretical, conceptual, methodological, and instructional issues in research on metacognition and self-regulated learning: A discussion. Metacognition and Learning, 4, 87–95. Scholar
  3. Azevedo, R., Moos, D., Johnson, A. M., & Chauncey, A. D. (2010). Measuring cognitive and metacognitive regulatory processes during hypermedia learning: Issues and challenges. Educational Psychologist, 45, 210–223. Scholar
  4. Barbaresi, W. J., Katusic, S. K., Colligan, R. C., Weaver, A. L., & Jacobsen, S. J. (2005). Learning disorder: Incidence in a population-based birth cohort, 1976–82, Rochester, Minn. Ambulatory Pediatrics, 5, 281–289.CrossRefGoogle Scholar
  5. Baten, E., & Desoete, A. (2018). Mathematical (dis)abilities within the opportunity–propensity model: The choice of math test matters. Frontiers in Psychology, Developmental Psychology. Scholar
  6. Baten, E., Praet, M., & Desoete, A. (2017). The relevance and efficacy of metacognition for instructional design in the domain of mathematics. ZDM Mathematics Education, 49, 613–623. Scholar
  7. Boekaerts, M., & Rozendaal, J. S. (2010). Using multiple calibrati on indices in order to capture the complex picture of what affects students’ accuracy of feeling of confidence. Learning and Instruction, 20, 372–382. Scholar
  8. Bol, L., & Hacker, D. J. (2012). Calibration research: Where do we go from here? Frontiers in Psychology, 3, 1–6. Scholar
  9. Borkowski, J. G. (1992). Metacognitive theory: A framework for teaching literacy, writing, and math skills. Journal of Learning Disabilities, 25, 253–257. Scholar
  10. Borkowski, J. G., & Thorpe, P. K. (1994). Self-regulation and motivation: A life-span perspective on underachievement. In D. H. Schunk & B. J. Zimmerman (Eds.), Selfregulation of learning and performance. Issues educational applications (pp. 45–100). Hillsdale: Erlbaum.Google Scholar
  11. Brown, A. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. Reiner & R. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 65–116). Hillsdale: Lawrence Erlbaum.Google Scholar
  12. Byrnes, J. P., & Miller, D. (2016). The growth of mathematics and reading skills in segregated and diverse schools: An opportunity–propensity analysis of a national database. Contemporary Educational Psychology, 46, 34–51. Scholar
  13. Byrnes, J. P., & Miller, D. C. (2007). The relative importance of predictors of math and science achievement: An opportunity–propensity analysis. Contemporary Educational Psychology, 32, 599–629. Scholar
  14. Byrnes, J. P., & Wasik, B. A. (2009). Factors predictive of mathematics achievement in kindergarten, first and third grades: An opportunity–propensity analysis. Contemporary Educational Psychology, 34, 167–183. Scholar
  15. Carr, M., Alexander, J., & Folds-Bennett, T. (1994). Metacognition and mathematics strategy use. Applied Cognitive Psychology, 8, 583–595. Scholar
  16. Carr, M., & Biddlecomb, B. (1998). Metacognition in mathematics from a constructivist perspective. In D. J. Hacker, J. Dunloksy & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 69–91). Mahwah: Lawrence Erlbaum Associates Publishers.Google Scholar
  17. Carr, M., & Jessup, D. L. (1995). Cognitive and metacognitive predictors of arithmetic strategy use. Learning and Individual Differences, 7, 235–247. Scholar
  18. Chen, B., Vansteenkiste, M., Beyers, W., Boone, L., Deci, E. L., Van der Kaap-Deeder, J., & Verstuyf, J. (2015). Basic psychological need satisfaction, need frustration, and need strength across four cultures. Motivation and Emotion, 39, 216–236. Scholar
  19. Chen, P. P. (2002). Exploring the accuracy and predictability of the self-efficacy beliefs of seventh-grade mathematics students. Learning and Individual Differences, 14, 77–90. Scholar
  20. Claessens, A., & Engel, M. (2013). How important is where you start? Early mathematics knowledge and later school success. Teachers College Record, 115(6), 1–29. (ID Number: 16980).
  21. Cohen Kadosh, R., & Dowker, A. (2015). The Oxford handbook of numerical cognition. Oxford: Oxford University Press.Google Scholar
  22. De Vos, T. (1992). Tempo-test rekenen (number fact retrieval test). Nijmegen: Berkhout.Google Scholar
  23. Deci, E. L., Connell, J., & Ryan, R. (1989). Self determination in a work organization. Journal of Applied Psychology, 74(4), 580–590.CrossRefGoogle Scholar
  24. Desender, K., Van Opstal, F., & Van den Bussche, E. (2017). Subjective experience of difficulty depends on multiple cues. Scientific Reports, 7, 44222. Scholar
  25. Desoete, A. (2008). Multi-method assessment of metacognitive skills in elementary school children: How you test is what you get. Metacognition Learning, 3, 189–206. Scholar
  26. Desoete, A., & Roeyers, H. (2002). Off-line metacognition. A domain-specific retardation in young children with learning disabilities? Learning Disability Quarterly, 25, 123–139. Scholar
  27. Desoete, A., & Roeyers, H. (2005). Cognitive skills in mathematical problem solving in grade 3. Britisch Journal of Educational Psychology, 75, 119–138. Scholar
  28. Desoete, A., & Roeyers, H. (2006). Metacognitive macroevaluations in mathematical problemsolving. Learning and Instruction, 16, 12–25. Scholar
  29. Desoete, A., Roeyers, H., & Buysse, A. (2001). Metacognition and mathematical problem solving in grade 3. Journal of Learning Disabilities, 34, 435–449. Scholar
  30. Desoete, A., Roeyers, H., & De Clercq, A. (2003). Can offline metacognition enhance mathematical problem solving? Journal of Educational Psychology, 95, 188–200. Scholar
  31. Dowker, A. (2015). Individual differences in arithmetical abilities. The componential nature of arithmetic. In R. Cohen, Kadosh & A. Dowker (Eds.), The Oxford handbook of mathematical cognition (pp. 862–878). Oxford: Oxford University Press.Google Scholar
  32. Dowker, A. D. (2005). Individual differences in arithmetic. Implications for psychology, neuroscience and education. New York: Psychology Press.CrossRefGoogle Scholar
  33. Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., et al. (2007). School readiness and later achievement. Developmental Psychology, 43(6), 1428–1446. Scholar
  34. Duncan, G. J., & Magnuson, K. (2009). The nature and impact of early achievement skills, attention and behavior problems. Presented at the russel sage foundation conference on social inequality and educational outcomes, November 19–20.Google Scholar
  35. Flavel, J. H. (1979). Metacognition and cognitive monitoring. American Psychologist, 34, 906–911. Scholar
  36. Fleming, S. M., & Lau, H. C. (2014). How to measure metacognition? Frontiers in Human Neuroscience, 8, 443. Scholar
  37. Froiland, M. J., Davison, M. L., & Worrell, F. C. (2016). Aloha teachers: Teacher autonomy support promotes Native Hawaiian and Pacific Islander students’ motivation, school belonging, course-taking and math achievement. Social Psychology of Education, 19, 879–894. Scholar
  38. García, T., Rodríguez, C., González-Castro, P., González-Pienda, J. A., & Torrance, M. (2016). Elementary students’ metacognitive processes and post-performance calibration on mathematical problem-solving tasks. Metacognition and Learning, 11, 139–170.CrossRefGoogle Scholar
  39. Geary, D. C. (2011). Cognitive predictors of achievement growth in mathematics: A 5-year longitudinal study. Developmental Psychology, 47, 1539–1552. Scholar
  40. Ghesquière, P., & Ruijssenaars, A. (1994). Vlaamse normen voor studietoetsen rekenen en technisch lezen lager onderwijs [Dutch norms for tests of mathematics and reading in elementary school]. Leuven: KULCSBO.Google Scholar
  41. Ghesquière, P., Desoete A., & Andries, C. (2014). Actualisering van het standpunt in verband met de praktijk van attestering voor kinderen met een leerstoornis in het gewoon onderwijs. Zorg dragen voor kinderen en jongeren met leerproblemen. Handvaten voor een goede praktijk. [Actualisation of the point of view on attestation of learning disabilities in regular education. Taking care of children and youngsters with learning problems. Advices for good practices]. Leuven: Acco.Google Scholar
  42. Grégoire, J. (2000). Comparison of three short forms of the Wechsler Intelligence Scale for Children. Third edition (WISC-III). Revue Européenne de Psychologie Appliquée, 50, 437–441.Google Scholar
  43. Hacker, J. D., Bol, L., Horgan, D. D., & Rakow, E. A. (2000). Test prediction and performance in a classroom context. Journal of Educational Psychology, 92, 160–170.CrossRefGoogle Scholar
  44. Jordan, N. C., Glutting, J., & Ramineni, C. (2010). The importance of number sense to mathematics achievement in first and third grades. Learning and Individual Differences, 20, 82–88. Scholar
  45. Jordan, N. C., & Kaplan, D. (2009). Early math matters: Kindergarten number competence and later mathematics outcomes. Developmental Psychology, 45, 850–867. Scholar
  46. Koriat, A. (2007). Metacognition and consciousness. In P. D. Zelazo, M. Moscovitch & E. Thompson (Eds.), The Cambridge handbook of consciousness (pp. 289–325). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  47. Kort, W., Schittekatte, M., Bosmans, M., Compaan, E. L., Dekker, P. H., Vermeir, G., et al. (2005). WISC-III-NL Wechsler Intelligence Scale For Children, Derde editie NL. Handleiding en verantwoording [WISC-III-NL Wechsler Intelligence Scale For Children, Thrid edition NL Manual and reasoning]. Amsterdam: Harcourt Test Publishers/Nederlands Instituut voor Psychologen.Google Scholar
  48. Kriegbaum, K., Jansen, M., & Spinath, B. (2015). Motivation: A predictor of PISA’s mathematical competence beyond intelligence and prior test achievement. Learning and Individual Differences, 43, 140–148. Scholar
  49. Kruger, J. (2002). Unskilled and unaware—but why? A reply to Krueger and Mueller. Journal of Personality and Social Psychology, 82, 189–192.CrossRefGoogle Scholar
  50. Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77, 1121–1134.CrossRefGoogle Scholar
  51. Lin, L., Moore, D., & Zabrucky, K. M. (2001). An assessment of student’s calibration of comprehension and calibration of performance using multiple measures. Reading Psychology, 22, 111–128. Scholar
  52. Lucangeli, D., Cornoldi, C., & Tellarini, M. (1998). Metacognition and learning disabilities in mathematics. In T. E. Scruggs & M. A. Mastropieri (Eds.), Advances in learning and behavioral disabilities (pp. 219–285). Greenwich: JAI.Google Scholar
  53. Mageau, G., & Vallerand, R. J. (2003). The coach-athlete relationship: a motivational model. Journal of Sports Sciences, 21, 883–904. Scholar
  54. Morosanova, V. I., Gomina, T. G., Kovas, Y., & Bogdanova, O. Y. (2016). Cognitive and regulatory characteristics and mathematical performances in high school students. Personality and Individual Differences, 90, 177–186. Scholar
  55. Özsoy, G. (2011). An investigation of the relationship between metacognition and mathematics achievement. Asia Pacific Education Review, 12, 227–235. Scholar
  56. Özsoy, G., & Ataman, A. (2009). The effect of metacognitive strategy training on mathematical problem solving achievement. International Electronic Journal of Elementary Education, 2, 67–82.
  57. Pieters, S., Roeyers, H., Rosseel, Y., Van Waelvelde, H., & Desoete, A. (2015). Identifying subtypes among children with developmental coordination disorder and mathematical learning disabilities, using model-based clustering. Journal of Learning Disabilities, 48, 83–95. Scholar
  58. Reeve, J. (2009). Why teachers adopt a controlling motivating style toward students and how they can become more autonomy supportive. Educational Psychologist, 44, 159–175. Scholar
  59. Reeve, J. (2016). Autonomy-supportive teaching: What it is, how to do it. In Building autonomous learners (pp. 129–152). Singapore: Springer. Scholar
  60. Ryan, R. M., & Deci, E. L. (2002). Handbook of self-determination. Rochester: The University of Rochester Press.Google Scholar
  61. Schneider, W., & Artelt, C. (2010). Metacognition and mathematics education. ZDM—The International Journal on Mathematics Education, 42, 149–161. Scholar
  62. Schneider, W., & Lockl, K. (2002). The development of metacognitive knowledge in children and adolescents. In T. Perfect & B. Schwartz (Eds.), Applied metacognition (pp. 224–247). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  63. Schraw, G., Kuch, F., & Gutierrez, A. P. (2013). Measure for measure: Calibrating ten commonly used calibration scores. Learning and Instruction, 24, 48–57. Scholar
  64. Schraw, G., Kuch, F., Gutierrez, A. P., & Richmond, A. S. (2014). Exploring a three-level model of calibration accuracy. Journal of Educational Psychology, 106, 1192–1202. Scholar
  65. Spinath, B., Spinath, F. M., Harlaar, N., & Plomin, R. (2006). Predicting school achievement from general cognitive ability, self-perceived ability, and intrinsic value. Intelligence, 34(4), 363–374. Scholar
  66. Spinath, B., Freudenthaler, H. H., & Neubauer, A. C. (2010). Domain-specific school achievement in boys and girls as predicted by intelligence, personality and motivation. Personality and Individual Differences, 48, 481–486. Scholar
  67. Steinmayer, R., & Spinath, B. (2009). The importance of motivation as a predictor of school achievement. Learning and Individual Differences, 19, 80–90. Scholar
  68. Tarricone, P. (2011). The taxonomy of metacognition. Hove: Psychology Press.Google Scholar
  69. Taylor, G., Jungert, T., Mageau, G. A., Schattke, K., Dedic, H., Rosenfield, S., & Koestner, R. (2014). A self-determination theory approach to predicting school achievement over time: The unique role of intrinsic motivation. Contemporary Educational Psychology, 39, 342–358. Scholar
  70. Van der Stel, M., & Veenman, M. (2014). Metacognitive skills and intellectual ability of young adolescents: A longitudinal study from a developmental perspective. European Journal of Psychological Studies, 29, 117–137. Scholar
  71. Van Petegem, S., Soenens, B., Vansteenkiste, M., & Beyers, W. (2015). Rebels with a cause? Adolescent defiance from the perspective of reactance theory and self-determination theory. Child Development, 86, 903–918. Scholar
  72. Vansteenkiste, M., Lens, W., Elliot, A. J., Soenens, B., & Mouratidis, A. (2014). Moving the achievement goal approach one step forward: Toward a systematic examination of the autonomous and controlled reasons underlying achievement goals. Educational Psychologist, 49, 153–174. Scholar
  73. Vansteenkiste, M., Sierens, E., Soenens, B., Luyckx, K., & Lens, W. (2009). Motivational profiles from a self-determination perspective: The quality of motivation matters. Journal of Educational Psychology, 3, 671–688. Scholar
  74. Veenman, M. V. J. (2011). Alternative assessment of strategy use with self-report instruments: A discussion. Metacognition and Learning, 6, 205–211. Scholar
  75. Veenman, M. V. J., & Spaans, M. A. (2005). Relation between intellectual and metacognitive skills: Age and task differences. Learning and Individual Differences, 15, 159–176. Scholar
  76. Veenman, M. V. J., Van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning. Conceptual and methodological considerations. Metacognition Learning, 1, 3–14. Scholar
  77. Vermeer, H. J., Boekaerts, M., & Seegers, G. (2000). Motivational and gender differences: Sixth-grade students’ mathematical problem-solving behavior. Journal of Educational Psychology, 92, 308–315. Scholar
  78. Verschaffel, L. (1999). Realistic mathematical modelling and problem solving in the upper elementary school: Analysis and improvement. In J. H. M. Hamers, J. E. H. Van Luit & B. Csapo (Eds.), Teaching and learning thinking skills. Contexts of learning (pp. 215–240). Lisse: Swets & Zeitlinger.Google Scholar
  79. Viljaranta, J., Lerkkanen, M., Poikkeus, K., Aunola, K., & Nurmi, J. (2009). Cross-lagged relations between task motivation and performance in arithmetic and literacy in kindergarten. Learning and Instruction, 19, 355–344. Scholar
  80. Wall, J. L., Thompson, C. A., Dunlosky, J., & Merriman, W. E. (2016). Children can accurately monitor and control their number-line estimation performance. Developmental Psychology, 2, 1493–1502. Scholar
  81. Wang, A. H., Shen, F., & Byrnes, J. P. (2013). Does the opportunity–propensity framework predict the early mathematics skills of low-income pre-kindergarten children? Contemporary Educational Psychology, 38, 259–270. Scholar
  82. Wechsler, D. (1991). Wechsler Intelligence Scale for Children (3rd ed.). New York: Psychological Corporation.Google Scholar
  83. Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. E. Pintrich & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). San Diego: Academic Press.CrossRefGoogle Scholar

Copyright information

© FIZ Karlsruhe 2019

Authors and Affiliations

  1. 1.Ghent UniversityGhentBelgium

Personalised recommendations