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Numerous measures exist to gain a full picture of a student's learning strengths and challenges. Following are examples of measures used to assess this Learner Factor. These measures should be administered and interpreted by experienced professionals.
Raven's Standard Progressive Matrices (Raven, 1981): A paper and pencil measure of non-verbal critical thinking and fluid intelligence in students six years and older.
Andersson, U. (2008). Working memory as a predictor of written arithmetical skills in children: The importance of central executive functions. The British Journal of Educational Psychology, 78, 181-203.
Bandurski, M., & Gałkowski, T. (2004). The development of analogical reasoning in deaf children and their parents' communication mode. Journal of Deaf Studies and Deaf Education, 9(2), 153-175.
Ferrer, E., & Mcardle, J. J. (2004). An experimental analysis of dynamic hypotheses about cognitive abilities and achievement from childhood to early adulthood. Developmental Psychology, 40(6), 935-952.
Fry, A. F., & Hale, S. (2000). Relationships among processing speed, working memory, and fluid intelligence in children. Biological Psychology, 54, 1-34.
Green, C. T., Bunge, S. A., Briones Chiongbian, V., Barrow, M., & Ferrer, E. (2017). Fluid reasoning predicts future mathematical performance among children and adolescents. Journal of Experimental Child Psychology, 157, 125-143.
Handley, S. J., Capon, A., Beveridge, M., Dennis, I., & Evans, J. S. B. T. (2017). Working memory, inhibitory control and the development of children's reasoning. Thinking and Reasoning, 10(2), 175-195.
Kane, M. J., Tuholski, S. W., Hambrick, D. Z., Wilhelm, O., Payne, T. W., & Engle, R. W. (2004). The generality of working memory capacity: A latent-variable approach to verbal and visuospatial memory span and reasoning. Journal of Experimental Psychology: General, 133(2), 189-217.
Kyttala, M., & Lehto, J. E. (2008). Some factors underlying mathematical performance: The role of visuospatial working memory and non-verbal intelligence. European Journal of Psychology of Education, 23(1), 77-94.
Primi, R., Ferrao, M. E., & Almeida, L. S. (2010). Fluid intelligence as a predictor of learning: A longitudinal multilevel approach applied to math. Learning and Individual Differences, 20(5), 446-451.
Raven, J. (1981). Manual for Raven's Progressive Matrices and Vocabulary scales. Research supplement no. 1: The 1979 British standardisation of the standard progressive matrices and mill hill vocabulary scales, together with comparative data from earlier studies in the UK, US, Canada, Germany and Ireland. Oxford: Oxford University Press; San Antonio, TX: The Psychological Corporation.
Susac, A., Bubic, A., Vrbanc, A., & Planinic, M. (2014). Development of abstract mathematical reasoning: The case of algebra. Frontiers in Human Neuroscience, 8(679), 1-10.
Taub, G. E., Floyd, R. G., Keith, T. Z., & McGrew, K. S. (2008). Effects of general and broad cognitive abilities on mathematics achievement. School Psychology Quarterly, 23(2), 187-198.
Tourva, A., Spanoudis, G., & Demetriou, A. (2016). Cognitive correlates of developing intelligence: The contribution of working memory, processing speed and attention. Intelligence, 54, 136-146.
van der Sluis, S., de Jong, P. F., & van der Leij, A. (2007). Executive functioning in children, and its relations with reasoning, reading, and arithmetic. Intelligence, 35(5), 427-449.
Wu, S. S., Chen, L., Battista, C., Smith Watts, A. K., Willcutt, E. G., & Menon, V. (2017). Distinct influences of affective and cognitive factors on children's non-verbal and verbal mathematical abilities. Cognition, 166, 118-129.
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