Análisis factorial confirmatorio del IPAM en escolares de tercer curso de primaria

  1. Jiménez, Juan E. 1
  2. de León, Sara C. 1
  1. 1 Universidad de La Laguna.
Journal:
Revista Evaluar

ISSN: 1667-4545

Year of publication: 2017

Volume: 17

Issue: 2

Type: Article

DOI: 10.35670/1667-4545.V17.N2.18723 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista Evaluar

Abstract

This study has been designed to analyse the factorial structure of IPAM using Confirmatory Factorial Analysis (CFA) techniques. For this purpose, a longitudinal study was carried out with a sample of 234 third-grade elementary students from the Canary Islands, to whom the instrument IPAM (Mathematics Learning Progress Indicators) was administered. IPAM is a curriculum-based measurement (CBM) instrument for universal screening and mathemat-ics learning progress monitoring in elementary grades. It is composed by three parallel measurements (A, B and C) that aim to measure the same latent structure (i.e., number sense) through the assessment of five indicators of basic early math skills using fluency tasks (i.e., magnitude com-parison, two-digit operations, missing number, one-digit operations, position value). IPAM was administered three times throughout the school year (i.e., fall, winter, and spring). The model tested showed a good fit at the different moments of measurement.

Bibliographic References

  • Andrews, P., & Sayers, J. (2015). Identifying opportunities for grade one children to acquire foundational number sense: Developing a framework for cross cultural classroom analyses. Early Childhood Education Journal, 43(4), 257-267. doi: 10.1007/s10643-014-0653-6
  • Cragg, L., & Gilmore, C. (2014). Skills underlying ma-thematics: The role of executive function in the development of mathematics proficiency. Trends in Neuroscience and Education, 3(2), 63-68. doi: 10.1016/j.tine.2013.12.001
  • Dehaene, S. (2009). Origins of mathematical intui-tions. The case of arithmetic. Annals of the New York Academy of Sciences, 1156(1), 232-259. doi: 10.1111/j.1749-6632.2009.04469.x
  • De Smedt, B., Verschaffel, L., & Ghesquière, P. (2009). The predictive value of numerical magnitude comparison for individual differences in mathematics achievement. Journal of Experimental Child Psychology, 103(4), 469-479. doi: 10.1016/j.jecp.2009.01.010
  • Dyson, N. I., Jordan, N. C., & Glutting, J. (2011). A number sense intervention for low-income kindergart-ners at risk for mathematics difficulties. Journal of Learning Disabilities, 46(2), 166-181. doi: 10.1177/0022219411410233
  • Foegen, A., Jiban, C., & Deno, S. (2007). Progress monitoring measures in mathematics. A review of the literature. The Journal of Special Education, 41(2), 121-139. doi: 10.1177/00224669070410020101
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18, 39-50. doi: 10.2307/3151312
  • Fuchs, L. S., Fuchs, D., Compton, D. L., Powel, S. R., Seethaler, P. M., Capizzi, A. M. ... Fletcher, J. M. (2006). The cognitive correlates of third-grade skill in arithmetic, algorithmic computation, and arithmetic word problems. Journal of Educational Psychology, 98(1), 29-43. doi: 10.1037/0022-0663.98.1.29
  • Geary, D. C. (2013). Early foundations for mathematics learning and their relations to learning disabilities. Current Directions in Psychological Science, 22(1), 23-27. doi: 10.1177/0963721412469398
  • Gersten, R., Clarke, B., Jordan, N. C., Newman-Gonchar, R., Haymond, K., & Wilkins, C. (2012). Universal screening in mathematics for the primary grades: Beginnings of a research base. Council for Exceptional Children, 78(4), 423-445. doi: 10.1177/001440291207800403
  • Hernández, J. A., & Betancort, M. (2016). ULLRTool-box. Disponible en https://sites.google.com/site/ullrtoolbox
  • Hassinger-Das, B., Jordan, N. C., Glutting, J., Irwin, C., & Dyson, N. (2014). Domain-general mediators of the relation between kindergarten number sense and first-grade mathematics achievement. Journal of Experimental Child Psychology, 118, 78-92. doi: 10.1016/j.jecp.2013.09.008
  • Holloway, I. D., & Ansari, D. (2009). Mapping numerical magnitudes onto symbols: The numerical distance effect and individual differences in children’s mathematics achievement. Journal of Experimental Child Psychology, 103(1), 17-29. doi: 10.1016/j.jecp.2008.04.001
  • Jiménez, J. E., & De León, S. (2016). Indicadores de Progreso de Aprendizaje en Matemáticas (IPAM). Universidad de La Laguna. Manuscrito sin publicar.
  • Jiménez, J. E., & De León, S. (2017). Análisis factorial confirmatorio de Indicadores de Progreso de Aprendizaje en Matemáticas (IPAM) en escolares de primer curso de primaria. European Journal of Investigation in Health, Psychology and Education, 7, 31-45. Recuperado de https://formacionasunivep.com/ejih-pe/index.php/ejihpe
  • Jitendra, A. K., Dupuis, D. N., & Zaslofsky, A. F. (2014). Curriculum-based measurement and standards-based mathematics: Monitoring the arithmetic word problem-solving performance of third-grade students at risk for mathematics difficulties. Learning Disability Quarterly, 37(4), 241-251. doi: 10.1177/0731948713516766
  • Jordan, N. C., Gutting, J., & Ramineni, C. (2010). The importance of number sense to mathematics achievement in first and third grades. Learning and Individual Differences, 20, 82-88. doi: 10.1016/j.lindif.2009.07.004
  • Jordan, N. C., Kaplan, D., Ramineni, C., & Locuniak, M. N. (2009). Early math matters: Kindergarten number competence and later mathematics outcomes. Developmental Psychology, 45(3), 850-867. doi: 10.1037/a0014939
  • Jordan, N. C., & Levine, S. C. (2009). Socioeconomic variation, number competence, and mathematics learning difficulties in young children. Developmental Disabilities Research Reviews, 15(1), 60-68. doi: 10.1002/ddrr.46
  • Jöreskog, K., & Sörbom, D. (1996-2001). LISREL 8: User’s Reference Guide. Illinois: Scientific Software International, Lincolnwood.
  • Kim, D., Shin, J., & Lee, K. (2013). Exploring latent class based on growth rates in number sense ability. Asia Pacific Education Review, 14(3), 445-453, doi: 10.1007/s12564-013-9274-9
  • Kline, R. B. (2005). Principles and practice of structural equation modeling (2ª ed.). Nueva York, NY: Guilford Press.
  • Kolkman, M. E., Kroesbergen, E. H., & Leseman, P. P. M. (2013). Early numerical development and the role of non-symbolic and symbolic skills. Learning and Instruction, 25, 95-103. doi: 10.1016/j.learninstruc.2012.12.001
  • LeFevre, J., Berrigan, L., Vendetti, C., Kamawar, D., Bisanz, J., Skwarchuck, S. ... Smith-Chant, B. (2013). The role of executive attention in the acquisition of ma-thematical skills for children in grades 2 through 4. Journal of Experimental Child Psychology, 114(2), 243-261. doi: 10.1016/j.jecp.2012.10.005
  • Lemaire, P., & Lecacheur, M. (2011). Age-related changes in children’s executive functions and strategy selection: A study in computational estimation. Cognitive Development, 26, 282-294. doi: 10.1016/j.cogdev.2011.01.002
  • Lembke, E., & Foegen, A. (2009). Identifying early numeracy indicators for kindergarten and first-grade students. Learning Disabilities Research & Practice, 24(1), 12-20. doi: 10.1111/j.1540-5826.2008.01273.x
  • Libertus, M. E., Fiegenson, L., & Halberda, J. (2011). Preschool acuity of the approximate num-ber system correlates with school math ability. Developmental Science, 14(6), 1292-1300. doi: 10.1111/j.1467-7687.2011.01080.x
  • Libertus, M. E., Fiegenson, L., & Halberda, J. (2013). Is approximate number precision a stable predictor of math ability? Learning and Individual Differences, 25, 126-133. doi: 10.1016/j.lindif.2013.02.001
  • Lucangeli, D., Tressoldi, P. E., Bendotti, M., Bonanomi, M., & Siegel, L. S. (2003). Effective strategies for mental and written arithmetic calculation from the third to the fifth grade. Educational Psychology, 23(5), 507-520. doi: 10.1080/0144341032000123769
  • Lyons, I. M., & Beilock, S. L. (2011). Numerical ordering ability mediates the relation between number-sense and arithmetic competence. Cognition, 121(2), 256-261. doi: 10.1016/j.cognition.2011.07.009
  • Lyons, I. M., Price, G. R., Vaessen, A., Blomert, L., & Ansari, D. (2014). Numerical predictors of arithmetic success in grades 1-6. Developmental Science, 17(5), 714-726. doi: 10.1111/desc.12152
  • Namkung, J. M., & Fuchs, L. S. (2016). Cognitive predictors of calculations and number line estimation with whole numbers and fractions among at-risk students. Journal of Educational Psychology, 108(2), 214-228. doi: 10.1037/edu0000055
  • National Council of Teachers of Mathematics (2000). Principles and standards for school mathematics. Reston, VA: NCATE.
  • National Mathematics Advisory Panel (2008). Foundations for Success: The Final Report of the National Mathematics Advisory Panel. Washington, DC: Department of Education.
  • Park, J., & Brannon, E. M. (2014). Improving arithmetic performance with number sense training: An investi-gation of underlying mechanism. Cognition, 133(1), 188-200. doi: 10.1016/j.cognition.2014.06.011
  • Peng, P., Namkung, J. M., Fuchs, D., Fuchs, L. S., Patton, S., Yen, L. ... Hamlett, C. (2016). A longitudinal study on predictors of early calculation development among young children at risk for learning difficulties. Journal of Experimental Child Psychology, 152, 221-241. doi: 10.1016/j.jecp.2016.07.017
  • Piazza, M. (2010). Neurocognitive start-up tools for symbolic number representations. Trends in Cognitive Sciences, 14(12), 542-551. doi: 10.1016/j.tics.2010.09.008
  • R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Recuperado de http://www.R-project.org
  • Sasanguie, D., Göbel, S. M., Moll, K., Smets, K., & Reynvoet, B. (2013). Approximate number sense, symbolic number processing, or number-space mappings: What underlies mathematics achievement? Journal of Experimental Child Psychology, 114(3), 418-431. doi: 10.1016/j.jecp.2012.10.012
  • Sayers, J., & Andrews, P. (2015). Foundational number sense: Summarising the development of an analyti-cal framework. En K. Krainer & N. Vondrová (Eds.), Ninth Congress of the European Society for Research in Mathematics Education (CERME9) (pp. 361-337). Praga: Charles University in Prague, Faculty of Education.
  • Schneider, M., Beeres, K., Coban, L., Merz, S., Schmidt, S. S., Stricker, J. ... De Smedt, B. (2016). Associations of non-symbolic and symbolic numerical magnitude processing with mathematical competence: A meta-analysis. Developmental Science, 20(3), 1-16. doi: 10.1111/desc.12372
  • Siegler, R., & Araya, R. (2005). A computational model of conscious and unconscious strategy discovery. Advances in Child Development and Behavior, 33, 1-42. doi: 10.1016/S0065-2407(05)80003-5
  • Sisco-Taylor, D., Fung, W., & Swanson, H. L. (2015). Do curriculum-based measures predict performance on word-problem-solving measures? Assessment for Effective Intervention, 40(3), 131-142. doi: 10.1177/1534508414556504
  • Starr, A., Libertus, M. E., & Brannon, E. M. (2013). Number sense in infancy predicts mathematical abilities in childhood. Proceedings of the National Academy of Science, 110(45), 18116-18120. doi: 10.1073/pnas.1302751110
  • Toll, S. W. M., Kroesbergen, E. H., & Van Luit, J. E. H. (2016). Visual working memory and number sense: Testing the double deficit hypothesis in mathematics. British Journal of Educational Psychology, 86(3), 429-445. doi: 10.1111/bjep.12116
Data source: Dialnet