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Education and Intelligence: Pity the Poor Teacher because Student Characteristics are more Significant than Teachers or Schools

Published online by Cambridge University Press:  06 December 2016

Douglas K. Detterman
Douglas K. Detterman*
Affiliation:
Case Western Reserve University (USA)
*
*Correspondence concerning this article should be addressed to Douglas K. Detterman. Case Western Reserve University. Cleveland, Ohio (USA). E-mail: detterman@case.edu)
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Abstract

Education has not changed from the beginning of recorded history. The problem is that focus has been on schools and teachers and not students. Here is a simple thought experiment with two conditions: 1) 50 teachers are assigned by their teaching quality to randomly composed classes of 20 students, 2) 50 classes of 20 each are composed by selecting the most able students to fill each class in order and teachers are assigned randomly to classes. In condition 1, teaching ability of each teacher and in condition 2, mean ability level of students in each class is correlated with average gain over the course of instruction. Educational gain will be best predicted by student abilities (up to r = 0.95) and much less by teachers’ skill (up to r = 0.32). I argue that seemingly immutable education will not change until we fully understand students and particularly human intelligence. Over the last 50 years in developed countries, evidence has accumulated that only about 10% of school achievement can be attributed to schools and teachers while the remaining 90% is due to characteristics associated with students. Teachers account for from 1% to 7% of total variance at every level of education. For students, intelligence accounts for much of the 90% of variance associated with learning gains. This evidence is reviewed.

Keywords

educationintelligenceschoolingteachers
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 19 , 2016 , E93
Copyright
Copyright © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2016 

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References

Angoff, W. H., & Johnson, E. G. (1990). The differential impact of curriculum on aptitude test scores. Journal of Educational Measurement, 27, 291–305. http://dx.doi.org/10.1111/j.1745-3984.1990.tb00750.x CrossRefGoogle Scholar
Asbury, K., & Plomin, R. (2013). G is for genes: The impact of genetics on education and achievement. Chichester, UK: Wiley-Blackwell.CrossRefGoogle Scholar
Baker, D. P., Goesling, B., & LeTendre, G. K. (2002). Socioeconomic status, school quality and national economic development: A cross-national analysis of the “Heyneman-Loxley effect” on mathematics and science achievement. Comparative Education Review, 46, 291–312. http://dx.doi.org/10.1086/341159 CrossRefGoogle Scholar
Basten, U., Hilger, K., & Feibach, C. J. (2015). Why smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27.Google Scholar
Binet, A., & Simon, T. (1905). Application des méthodes nouvelles au diagnositic du neveau intellectual chez des enfants normaux et anormaux d’hocpice et d’école primaire. L’Année Psychologique, 11, 245–336.Google Scholar
Bowen, W. G., & Bok, D. (1998). The shape of the river: Long-term consequences of considering race in college and university admissions. Princeton, NJ: Princeton University Press.Google Scholar
Byrne, B., Coventry, W. L., Olson, R. K., Wadsworth, S. J., Samuelsson, S., Petrill, S. A., … Corley, R. (2010). “Teacher effects” in early literacy development: Evidence from a study of twins. Journal of Educational Psychology, 102(1), 32–42. http://doi.org/10.1037/a0017288.Google Scholar
Ceci, S. J. (1991). How much does schooling influence general intelligence and its cognitive components? A reassessment of the evidence. Developmental Psychology, 27, 703–722. http://dx.doi.org/10.1037/0012-1649.27.5.703 Google Scholar
Chingos, M. M., Whitehurst, G. J. R., & Lindquist, K. M. (2014). School superintendants: Vital or irrelevant. Washington, DC: Brookings. Retrieved from http://www.brookings.edu/research/reports/2014/09/03-superintendents-chingos-whitehurst Google Scholar
Coalition for Evidence-Based Policy (2013). Randomized controlled trials commissioned by the Institute of Education Sciences since 2002: How many found positive versus weak or no effects. Washington, DC: Author. Retrieved from http://coalition4evidence.org/468-2/most-popular/ Google Scholar
Coleman, J. S., Campbell, E. Q., Hobson, C. J., Partland, F., Mood, A. M., Weinfeld, M. D., & York, R. L. (1966). Equality of educational opportunity. Washington, DC: Government Printing Office.Google Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006). Finding the g-factor in brain structure using the method of correlated vectors. Intelligence, 34, 561–570. http://dx.doi.org/10.1016/j.intell.2006.03.006 Google Scholar
Crano, W. D., Kenny, D. A., & Campbell, D. T. (1972). Does intelligence cause achievement? A cross-lagged panel analysis. Journal of Educational Psychology, 63, 258–275. http://dx.doi.org/10.1037/h0032639 Google Scholar
Dale, S., & Kruger, A. B. (2011). Estimating the return to college selectivity over the career using adminsitrative earnings data. The National Bureau of Economic Research, Paper 17159. Retrieved from http://www.nber.org/papers/w17159 Google Scholar
Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35, 13–21. http://dx.doi.org/10.1016/j.intell.2006.02.001 CrossRefGoogle Scholar
Deary, I. J., Whalley, L. J., Lemmon, H., Crawford, J. R., & Starr, J. M. (2000). The stability of individual differences from childhood to old age: Follow-up of the 1932 Scottish Mental Survey. Intelligence, 28, 49–55. http://dx.doi.org/10.1016/S0160-2896(99)00031-8 CrossRefGoogle Scholar
Detterman, D. K. (2014). Introduction to the Intelligence special issue on the development of expertise: Is ability necessary? Intelligence, 45, 1–5. http://dx.doi.org/10.1016/j.intell.2014.02.004 Google Scholar
Engelhardt, L. E., Briley, D. A., Mann, F. D., Harden, K. P., & Tucker-Drob, E. M. (2015). Genes unite executive functions in childhood. Developmental Psychology, 26, 1151–1163. http://dx.doi.org/10.1177/0956797615577209 Google ScholarPubMed
Firkowska, A., Ostrowska, A., Sokolowska, M., Stein, Z., Susser, M., & Wald, I. (1978). Cognitive development and social policy. Science, 200, 1357–1362. http://dx.doi.org/10.1126/science.663616 Google Scholar
Flynn, J. R. (1984). The mean IQ of Americans: Massive gains 1932 to 1978. Psychological Bulletin, 95, 29–51. http://dx.doi.org/10.1037/0033-2909.95.1.29 CrossRefGoogle Scholar
Galton, F. (1869). Hereditary genius. London, UK: Macmillan.CrossRefGoogle Scholar
Gamoran, A., & Long, D. A. (2006). Equality of educational opportunity: A 40-year retrospective. (WCER Working Paper No. 2006–9). Madison, WI: University of Wisconsin–Madison, Wisconsin Center for Education Research. Retrieved from http://www.wcer.wisc.edu/publications/workingPapers/papers.php Google Scholar
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Hong, L., Zijdenbos, A., … Rapoport, J. L. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2, 861–863. http://dx.doi.org/10.1038/13158 CrossRefGoogle ScholarPubMed
Haworth, C. M. A., Wright, M. J., Luciano, M., Martin, N. G., de Geus, E. J. C., van Beijsterveldt, C. E. M., … Plomin, R. (2010). The heritability of general cognitive ability increases linearly from childhood to young adulthood. Molecular Psychiatry, 15, 1112–1120. http://dx.doi.org/10.1038/mp.2009.55 CrossRefGoogle ScholarPubMed
Heyneman, S. P., & Loxley, W. A., (1983). The effect of primary school quality on academic achievement across twenty-nine high and low income countries. American Journal of Sociology, 88, 1162–1194. http://dx.doi.org/10.1086/227799 Google Scholar
Huarte, J. (1698). Examen de ingenios par las ciencias; or The trial of wits. Discovering the great differences among men, and what sort of learning suits best with each genius. Published in Spanish by Doctor Juan Huarte. And made English from the most correct edition by Mr. Bellamy. London, UK: Printed for Richard Sare, at Grays-inn-gate in Holborn.CrossRefGoogle Scholar
Jacob, B. A., Lefgren, L., & Sims, D. P. (2010). The persistence of teacher-induced learning. Journal of Human Resources, 45, 915–943.CrossRefGoogle Scholar
Jencks, C., Smith, M., Ackland, H., Bane, M. S., Cohen, D., Gintis, H., … Michelson, S. (1972). Inequality: A reassessment of the effect of family and schooling in America. New York, NY: Basic Books.Google Scholar
Johnson, A. (2016). Learning, the brain, and the teacher. Retrieved from www.academia.edu Google Scholar
Johnson, W., & Bouchard, T. J. Jr (2005a). The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized. Intelligence, 33, 393–416. http://dx.doi.org/10.1016/j.intell.2004.12.002 CrossRefGoogle Scholar
Johnson, W., & Bouchard, T. J. Jr (2005b). Constructive replication of the visual-perceptual-image rotation model in Thurstone’s (1941) battery of 60 tests of mental ability. Intelligence, 33, 417–430.CrossRefGoogle Scholar
Johnson, W., Bouchard, T. J. Jr, Krueger, R. F., McGue, M., & Gottesman, I. I. (2004). Just one g: Consistent results from three test batteries. Intelligence, 32, 95–107. http://dx.doi.org/10.1016/S0160-2896(03)00062-X CrossRefGoogle Scholar
Johnson, W., te Nijenhuis, J., & Bouchard, T. J Jr. (2008). Still just 1 g: Consistent results from five test batteries. Intelligence, 36, 81–95. http://dx.doi.org/10.1016/j.intell.2007.06.001 CrossRefGoogle Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30, 135–157. http://dx.doi.org/10.1017/S0140525X07001185 CrossRefGoogle ScholarPubMed
Kaufman, S. B., Reynolds, M. R., Liu, X., Kaufman, A. S., & McGrew, K. S. (2012). Are cognitive g and academic achievement g on and the same g? An exploration on the Woodcock-Johnson and Kaufman tests. Intelligence, 40, 123–138. http://dx.doi.org/10.1016/j.intell.2012.01.009 CrossRefGoogle Scholar
Krapohl, E., Rimfield, K., Shakeshaft, N. G., Trzaskowski, M., McMillan, A., Pingault, J. B., … Plomin, R. (2014). The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence. PNAS, 111, 15273–15278. http://dx.doi.org/10.1073/pnas.1408777111 Google Scholar
Kyllonen, P. C., & Christal, R. E. (1990). Reasoning ability is (little more than) working memory capacity?! Intelligence, 14, 389–433. http://dx.doi.org/10.1016/S0160-2896(05)80012-1 CrossRefGoogle Scholar
Luo, D., Thompson, L. A., & Detterman, D. K. (2003). The causal factor underlying the correlation between psychometric g and scholastic performance. Intelligence, 31, 67–83. http://dx.doi.org/10.1016/S0160-2896(02)00113-7 Google Scholar
Lynn, R., & Mikk, J. (2007). National differences in intelligence and educational attainment. Intelligence, 35, 115–121. http://dx.doi.org/10.1016/j.intell.2006.06.001 Google Scholar
Mosing, M. A., Madison, G., Pedersen, N. L., Kuja-Halkola, R., & Ullén, F. (2014). Practice does not make perfect: No causal effect of music practice on music ability. Psychological Science, 25, 1795–1803. http://dx.doi.org/10.1177/0956797614541990 Google Scholar
Petrill, S. A., & Wilkerson, B. (2000). Intelligence and achievement: A behavioral genetic perspective. Educational Psychology Review, 12, 185–199.CrossRefGoogle Scholar
Plomin, R., DeFries, J. C., Knopik, V. S., & Neidherhiser, J. M. (2016). Top 10 replicated findings form behavioral genetics. Perspectives on Psychological Science, 11, 3–23. http://dx.doi.org/10.1177/1745691615617439 CrossRefGoogle Scholar
Plomin, R., & Kovas, Y. (2005). Generalist genes and learning disabilities. Psychological Bulletin, 131, 592–617. http://dx.doi.org/10.1037/0033-2909.131.4.592 Google Scholar
Spearman, C. E. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15, 201–292. http://dx.doi.org/10.2307/1412107 Google Scholar
The New Teacher Project. (2015). The mirage: Confronting the hard truth about our quest for teacher development. Brooklyn, NY: Author. Retrieved from http://tntp.org/publications/view/the-mirage-confronting-the-truth-about-our-quest-for-teacher-development Google Scholar
Thompson, L. A., Detterman, D. K., & Plomin, R. (1991). Associations between cognitive abilities and scholastic achievement: Genetic overlap but environmental differences. Psychological Science, 2, 158–165. http://dx.doi.org/10.1111/j.1467-9280.1991.tb00124.x Google Scholar
Tuddenham, R. D. (1948). Soldier intelligence in World Wars I and II. American Psychologist, 3, 54–56. http://dx.doi.org/10.1037/h0054962 Google Scholar
Watkins, M. W., Lei, P-W., & Canivez, G. L. (2007). Psychometric intelligence and achievement: A cross-lagged panel analysis. Intelligence, 35, 59–68. http://dx.doi.org/10.1016/j.intell.2006.04.005 CrossRefGoogle Scholar
Whitehurst, G. J. R., Chingos, M. M., & Gallaher, M. R. (2013). Do school districts matter? Washington, DC: Brookings. Retrieved from http://www.brookings.edu/research/papers/2013/03/27-school-district-reform-whitehurst Google Scholar