9.4 Chapter Summary
Learning Myth Buster: The Myth — Students learn best when taught according to their learning styles
The idea of learning styles is that some individuals learn best through a specific mode of instruction. This is a very popular notion that has been widely adopted by the educational system. There are many websites that claim to identify your best learning style, for example, the popular “VARK” model classifies individuals as visual, auditory, read/write or kinesthetic learners (Fleming & Mills, 1992). Other models classify learners as “accommodating, diverging, converging, and assimilating.” Despite the popularity of these classifications, the psychological research does not support the idea that being taught in a manner consistent with a learning style enhances academic performance (Pashler et al., 2009). For example, Husmann and O’Loughlin (2019) tested students’ learning of anatomy as related to their purported learning style. Their results showed no correlation of students’ learning style with performance in the course or with their method of studying. Rather, all students benefitted from engaging learning techniques such as studying with a virtual microscope. The meta-analysis by Pashler et al. (2009) documented that people have preferences for how they like information presented to them. However, they found no evidence to support that matching teaching and learning styles lead to better learning. They conclude that learning-styles assessments in educational settings is “unwise and a wasteful use of limited resources” (Pashler et al., 2009, p. 117).
For a news release on Pashler et al.’s article see: APS: Learning styles debunked
Intelligence, gender, and temperament are characteristics of an individual that can influence learning. These characteristics are not set at birth, they are modifiable by a person’s social interactions and environment. The role of mindset, grit, and learning styles in learning is less well supported by the research. Those differences that do exist, are often of a small effect size. When considering educational implications of individual learning characteristics, it is important to focus on those differences of large effect size, such as intelligence or writing differences based on gender, rather than the differences with small effect sizes such as those seen in early math and science.
References
Ackerman, P. L., Beier, M. E., & Boyle, M. O. (2005). Working memory and intelligence: The same or different constructs? Psychological Bulletin, 131(1), 30–60.
Andreasen, N. C., Flaum, M., Victor Swayze, I. I., O’Leary, D. S., Alliger, R., & Cohen, G. (1993). Intelligence and brain structure in normal individuals. Am J Psychiatry. 1, 130-134. Retrieved From Wikipedia: Digital object identifier / Psychiatry Online
Asperholm, M., Hogman, N., Rafi, J., & Herlitz, A. (2019). What did you do yesterday? A meta-anlysis of sex differences in episodic memory. Psychology Bulletin, 145, 785-821.
Baltes, P. B., & Reinert, G. (1969). Cohort effects in cognitive development of children as revealed by cross-sectional sequences. Developmental Psychology, 1(2), 169–177.
Bellinger, D. C., & Needleman, H. L. (2003). Intellectual impairment and blood lead levels [Letter to the editor]. The New England Journal of Medicine, 349(5), 500.
Bramlett, R., Scott, P., & Roswell, R. (2000). A comparison of temperament and social skills in predicting academic performance in first graders. Special Services in Schools, 16, 147-158.
Brody, N. (2003). Construct validation of the Sternberg Triarchic abilities test: Comment and reanalysis. Intelligence, 31(4), 319–329.
Brooks-Gunn, J., & Duncan, G. J. (1997). The effects of poverty on children. The Future of Children, 7(2), 55–71.
Burnette, J. L., O’Boyle, E. H., VanEpps, E. M., Pollack, J. M., & Finkel, E. J. (2013). Mind-sets matter: A meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139(3), 655-701.
Byrnes, J. (2001). Cognitive development and learning in instructional contexts. (2nd ed.). Boston: Allyn and Bacon
Ceci, S. J. (1991). How much does schooling influence general intelligence and its cognitive components? A reassessment of the evidence. Developmental Psychology, 27(5), 703–722.
Ceci, S. J., & Williams, W. M. (1997). Schooling, intelligence, and income. American Psychologist, 52(10), 1051–1058.
Chess, A., & Chess, S. (1977). Temperament and development. NY: Brunner/Mazel.
Chess, S. & Thomas A. (1987). Temperamental individuality from childhood to adolescence. Journal of Child Psychiatry, 16, 218-26.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. 2nd ed. Erlbaum.
Deary, I. J., Der, G., & Ford, G. (2001). Reaction times and intelligence differences: A population-based cohort study. Intelligence, 29(5), 389–399.
Deary, I. J., Whiteman, M. C., Starr, J. M., Whalley, L. J., & Fox, H. C. (2004). The impact of childhood intelligence on later life: Following up the Scottish mental surveys of 1932 and 1947. Journal of Personality and Social Psychology, 86(1), 130–147.
Duckworth, A., Peterson, C., Matthews, M., & Kelly, D. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92, 1087-1101.
Dugan, R., Hochstein, B., & Rouziou, M. (2019).The pros and cons of grit. Talent Development. 73, 44-48.
Dweck, C. (2006). Mindset: the new psychology of success. NY: Random House.
Fleming, N., & Mills, C. (1992). Not another inventory, rather a catalyst for reflection. To Improve the Academy, 11, 137-143.
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York, NY: Basic Books;
Gardner, H. (1999). Intelligence reframed: Multiple intelligences for the 21st century. New York, NY: Basic Books.
Gottfredson, L. S. (1997). Mainstream science on intelligence: An editorial with 52 signatories, history and bibliography. Intelligence, 24(1), 13–23.
Haier, R. J., Siegel, B. V., Tang, C., & Abel, L. (1992). Intelligence and changes in regional cerebral glucose metabolic rate following learning. Intelligence, 16(3–4), 415–426.
Husmann, P., & O’Loughlin, V. (2019). Another nail in the coffin for learning styles? Disparities among undergraduate anatomy students’ study strategies, class performance, and reported VARK learning styles. Anatomical Sciences Education, 12, 6–19.
Huttenlocher, J., Levine, S., & Vevea, J. (1998). Environmental input and cognitive growth: A study using time-period comparisons. Child Development, 69(4), 1012–1029.
Hyde, J.S. (2016). Sex and cognition: Gender and cognitive functions. Current Opinion in Neurobiology, 38, 53–56.
Ingalikar, M., Smith, A., Parker, D., Satterhwaite, P., Elliott, M., Ruparel, K., et al. (2013). Sex differences in the structural connectome of the human brain. Proceedings of the National Academy of Sciences. USA, 111, 823-828.
Kimball, M. (1989). A new perspective on women’s math achievement. Psychological Bulletin, 105, 198-214.
Kimura, D. (1999). Sex and cognition. Cambridge, MA: MIT Press.
McLoyd, V. C. (1998). Children in poverty: Development, public policy and practice. In W. Damon, I. E. Sigel, & K. A. Renninger (Eds.), Handbook of child psychology: Child psychology in practice (5th ed., Vol. 4, pp. 135–208). Hoboken, NJ: John Wiley & Sons.
Neisser, U., Boodoo, G., Bouchard, T. J., Jr., Boykin, A. W., Brody, N., Ceci, S. J.,…Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51(2), 77–101.
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105-119. Retrieved from Sage journals
Perkins, D. N., & Grotzer, T. A. (1997). Teaching intelligence. American Psychologist, 52(10), 1125–1133.
Plomin, R. (2003). General cognitive ability. In R. Plomin, J. C. DeFries, I. W. Craig, & P. McGuffin (Eds.), Behavioral genetics in the postgenomic era (pp. 183–201). Washington, DC: American Psychological Association.
Plomin, R., & Spinath, F. M. (2004). Intelligence: Genetics, genes, and genomics. Journal of Personality and Social Psychology, 86(1), 112–129.
Posner, M. I., & Rothbart, M. K. (2007). Temperament and learning. In M. I. Posner & M. K. Rothbart, Educating the human brain (pp. 121-146). Washington, DC, US: American Psychological Association.
Rothbatt, M., & Bates, J.(2006). Temperament in children’s development. In W. Damon & R. Lerner (Series Eds.) & N. Eisenberg (Vol. Ed.) Handbook of child psychology: Vol. 3. Social, emotional and personality development.(6th ed.), 99-166). NY: Wiley.
Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., & Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychological Science, 29(4), 549–571. Retrieved from Sage journals
Sternberg, R. J. (2003). Our research program validating the triarchic theory of successful intelligence: Reply to Gottfredson. Intelligence, 31(4), 399–413.
Tarasova, I. V., Volf, N. V., & Razoumnikova, O. M. (2010). Parameters of cortical interactions in subjects with high and low levels of verbal creativity. Human Physiology, 36(1), 80–85.
Treffert, D. A., & Wallace, G. L. (2004, January 1). Islands of genius. Scientific American, 14–23. Retrieved from Academia
Turkheimer, E., Haley, A., Waldron, M., D’Onofrio, B., & Gottesman, I. I. (2003). Socioeconomic status modifies heritability of IQ in young children. Psychological Science, 14(6), 623–628.
Usher, E., Li, C., Butz, A., & Rojas, J. (2019) Perseverant grit and self-efficacy: Are both essential for children’s academic success? Journal of Educational Psychology, 111, 877-902.
Uttal, D., Meadow, N., Tipton, E., Hand, L., Alden, A., Warren, C., & Newcombe, N. (2013). The malleability of spatial skills: A meta-analysis of training studies. Psychological Bulletin, 139, 352-402.
Voyer, D. & Voyer S. (2014). Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin, 140, 1174-1204.
Voyer, D., Voyer, S., & Bryden, M. (1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin, 117, 250-270.
Witelson, S. F., Beresh, H., & Kigar, D. L. (2006). Intelligence and brain size in 100 postmortem brains: Sex, lateralization and age factors. Brain, 129 (2). 386–398. Retrieved from Wikipedia: Digital object identifier / Oxford Academic / Wikipedia: PubMed / PubMed
Zell, E. ,Krizan, Z.,& Teeter, S. (2015). Evaluating gender similarities and differences using metasynthesis, American Psychologist, 70(1), 10-20.