Executive functions in the context of complex learning: Malleable moderators?

Main Article Content

Matthias Schwaighofer
Markus Bühner
Frank Fischer

Abstract

Executive functions are crucial for complex learning in addition to prior knowledge. In this article, we argue that executive functions can moderate the effectiveness of instructional approaches that vary with respect to the demand on these functions. In addition, we suggest that engagement in complex activity contexts rather than specific cognitive training paradigms may enhance executive functions and yield practically relevant transfer effects to other cognitive abilities. We develop several hypotheses and principles for how to improve executive functions in these contexts. For future research, we suggest to systematically investigate the moderating role of executive functions in learning environments with varying degrees of instructional support and varying context characteristics. We identify potential factors influencing the improvement of executive functions to be considered in a systematic research program.

Article Details

How to Cite
Schwaighofer, M., Bühner, M., & Fischer, F. (2017). Executive functions in the context of complex learning: Malleable moderators?. Frontline Learning Research, 5(1), 58–75. https://doi.org/10.14786/flr.v5i1.268
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References

Ackerman, P. L., Beier, M. E., & Boyle, M. O. (2002). Individual differences in working memory within a nomological network of cognitive and perceptual speed abilities. Journal of Experimental Psychology. General, 131(4), 567–589. doi: 10.1037/0096-3445.131.4.567
Agostino, A., Johnson, J., & Pascual-Leone, J. (2010). Executive functions underlying multiplicative reasoning: Problem type matters. Journal of Experimental Child Psychology, 105(4), 286–305. doi: 10.1016/j.jecp.2009.09.006
Andersson, U. (2010). Skill development in different components of arithmetic and basic cognitive functions: Findings from a 3-year longitudinal study of children with different types of learning difficulties. Journal of Educational Psychology, 102(1), 115–134. doi:10.1037/a0016838
Baddeley, A. D., Allen, R. J., & Hitch, G. J. (2011). Binding in visual working memory: The role of the episodic buffer. Neuropsychologia, 49(6), 1393–1400. doi: 10.1016/j.neuropsychologia.2010.12.042
Berends, I. E., & van Lieshout, E. C. D. M. (2009). The effect of illustrations in arithmetic problem-solving: Effects of increased cognitive load. Learning and Instruction, 19, 345–353. doi: 10.1016/j.learninstruc.2008.06.012
Blair, C., Knipe, H., & Gamson, D. (2008). Is there a role for executive functions in the development of mathematics ability? Mind, Brain, and Education, 2(2), 80–89. doi: 10.1111/j.1751-228X.2008.00036.x
Blair, C., & Raver, C. C. (2014). Closing the achievement gap through modification of neurocognitive and neuroendocrine function: Results from a cluster randomized controlled trial of an innovative approach to the education of children in kindergarten. PLoS ONE, 9(11), e112393. doi: 10.1371/journal.pone.0112393
Blair, C., & Ursache, A. (2011). A bidirectional theory of executive functions and self-regulation. In K. Vohs, & R. Baumeister (Eds.), Handbook of self-regulation. (2 ed., pp. 300-320). New York: Guilford Press.
Blair, C., Ursache, A., Greenberg, M., Vernon-Feagans, L., & The Family Life Project Investigators (2015). Multiple aspects of self-regulation uniquely predict mathematics but not letter–word knowledge in the early elementary grades. Developmental Psychology, 51(4), 459–472. doi: 10.1037/a0038813
Borella, E., & de Ribaupierre, A. (2014). The role of working memory, inhibition, and processing speed in text comprehension in children. Learning and Individual Differences, 34, 86–92. doi: 10.1016/j.lindif.2014.05.001
Bühner, M., Kröner, S., & Ziegler, M. (2008). Working memory, visual–spatial-intelligence and their relationship to problem-solving. Intelligence, 36(6), 672–680. doi:10.1016/j.intell.2008.03.008
Bull, R., & Lee, K. (2014). Executive functioning and mathematics achievement. Child Development Perspectives, 8(1), 36–41. doi: 10.1111/cdep.12059
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–185. doi:10.1017/S0140525X01003922
Daneman, M., & Merikle, P. M. (1996). Working memory and language comprehension: A meta-analysis. Psychonomic Bulletin & Review, 3(4), 422–433. doi: 10.3758/BF03214546
de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science, 38, 105–134. doi: 10.1007/s11251-009-9110-0
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64(1), 135–168. doi: 10.1146/annurev-psych-113011-143750
Diamond, A., Barnett, W. S., Thomas, J., & Munro, S. (2007). Preschool program improves cognitive control. Science, 318 (5855), 1387-1388. doi: 10.1126/science.1151148
Diamond, A., & Ling, D. S. (2016). Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Developmental Cognitive Neuroscience, 18, 34–48. doi: 10.1016/j.dcn.2015.11.005
Dochy, F., Segers, M., & Buehl, M. M. (1999). The relation between assessment practices and outcomes of studies: The case of research on prior knowledge. Review of Educational Research, 69(2), 145–186. doi:10.3102/00346543069002145
Draheim, C., Hicks, K. L., & Engle, R. W. (2016). Combining reaction time and accuracy: The relationship between working memory capacity and task switching as a case example. Perspectives on Psychological Science, 11(1), 133–155. doi: 10.1177/1745691615596990
Ecker, U. K. H., Lewandowsky, S., & Oberauer, K. (2014). Removal of information from working memory: A specific updating process. Journal of Memory and Language, 74, 77–90. doi: 10.1016/j.jml.2013.09.003
Ecker, U. K. H., Lewandowsky, S., Oberauer, K., & Chee, A. E. H. (2010). The components of working memory updating: An experimental decomposition and individual differences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(1), 170–189. doi: 10.1037/a0017891
Espy, K. A., McDiarmid, M. M., Cwik, M. F., Stalets, M. M., Hamby, A., & Senn, T. E. (2004). The contribution of executive functions to emergent mathematic skills in preschool children. Developmental Neuropsychology, 26, 465-486. doi: 10.1207/s15326942dn2601_6
Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7(2), 336–353. doi: 10.1037/1528-3542.7.2.336
Friedman, N. P., & Miyake, A. (2004). The relations among inhibition and interference control functions: A latent-variable analysis. Journal of Experimental Psychology. General, 133(1), 101–135. doi:10.1037/0096-3445.133.1.101
Friedman, N. P., & Miyake, A. (2017). Unity and diversity of executive functions: Individual differences as a window on cognitive structure. Cortex, 86, 186–204. doi: 10.1016/j.cortex.2016.04.023
Friedman, N. P., Miyake, A., Altamirano, L. J., Corley, R. P., Young, S. E., Rhea, S. A., & Hewitt, J. K. (2016). Stability and change in executive function abilities from late adolescence to early adulthood: A longitudinal twin study. Developmental Psychology, 52(2), 326–340. doi: 10.1037/dev0000075
Friedman, N. P., Miyake, A., Robinson, J. L., & Hewitt, J. K. (2011). Developmental trajectories in toddlers’ self-restraint predict individual differences in executive functions 14 years later: A behavioral genetic analysis. Developmental Psychology, 47(5), 1410–1430. doi: 10.1037/a0023750
Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008). Individual differences in executive functions are almost entirely genetic in origin. Journal of Experimental Psychology: General, 137(2), 201–225. doi: 10.1037/0096-3445.137.2.201
Gathercole, S. E., & Alloway, T. P. (2007). Understanding working memory. A classroom guide. London, UK: Harcourt Assessment. Retrieved from http://www.york.ac.uk/res/wml/Classroom%20guide.pdf
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Press.
Holmes, J., Gathercole, S. E., Place, M., Dunning, D. L., Hilton, K. A., & Elliott, J. G. (2010). Working memory deficits can be overcome: Impacts of training and medication on working memory in children with ADHD. Applied Cognitive Psychology, 24(6), 827–836. doi: 10.1002/acp.1589
Jacob, R., & Parkinson, J. (2015). The potential for school-based interventions that target executive function to improve academic achievement: A review. Review of Educational Research, 85(4), 512–552. doi: 10.3102/0034654314561338
Jacobson, L. A., Koriakin, T., Lipkin, P., Boada, R., Frijters, J. C., Lovett, M. W., … Mahone, E. M. (2016). Executive functions contribute uniquely to reading competence in minority youth. Journal of Learning Disabilities. doi: 10.1177/0022219415618501
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences, 105(19), 6829–6833.
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539. doi: 10.1007/s10648-007-9054-3
Kalyuga, S. (2013). Effects of learner prior knowledge and working memory limitations on multimedia learning. Procedia - Social and Behavioral Sciences, 83, 25–29. doi:10.1016/j.sbspro.2013.06.005
Kalyuga, S., Rikers, R., & Paas, F. (2012). Educational implications of expertise reversal effects in learning and performance of complex cognitive and sensorimotor skills. Educational Psychology Review, 24(2), 313–337. doi: 10.1007/s10648-012-9195-x
Kalyuga, S., & Singh, A.-M. (2015). Rethinking the boundaries of cognitive load theory in complex learning. Educational Psychology Review. doi: 10.1007/s10648-015-9352-0
Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96(3), 558–568. doi: 10.1037/0022-0663.96.3.558
Karbach, J., & Verhaeghen, P. (2014). Making working memory work: A meta-analysis of executive-control and working memory training in older adults. Psychological Science, 25(11), 2027–2037. doi: 10.1177/0956797614548725
Kester, L., & Kirschner, P. A. (2012). Cognitive tasks and learning. In N. Seel (Ed.), Encyclopedia of the Sciences of Learning (pp. 619-622). New York: Springer.
König, C. J., Bühner, M., & Mürling, G. (2005). Working memory, fluid intelligence, and attention are predictors of multitasking performance, but polychronicity and extraversion are not. Human Performance, 18(3), 243–266. doi:10.1207/s15327043hup1803_3
Kramer, A. F., & Erickson, K. I. (2007). Capitalizing on cortical plasticity: Influence of physical activity on cognition and brain function. Trends in Cognitive Sciences, 11(8), 342–348. doi: 10.1016/j.tics.2007.06.009
Krumm, S., Schmidt-Atzert, L., Bühner, M., Ziegler, M., Michalczyk, K., & Arrow, K. (2009). Storage and non-storage components of working memory predicting reasoning: A simultaneous examination of a wide range of ability factors. Intelligence, 37(4), 347–364. doi:10.1016/j.intell.2009.02.003
Lan, X., Legare, C., Ponitz, C. C., Li, S., & Morrison, F. J. (2011). Investigating the links between the subcomponents of executive function and academic achievement: A cross-cultural analysis of Chinese and American preschoolers. Journal of Experimental Child Psychology, 108, 677-692. doi:10.1016/j.jecp.2010.11.001
Lee, K., Bull, R., & Ho, R. M. H. (2013). Developmental changes in executive functioning. Child Development, 84, 1933–1953. doi: 10.1111/cdev.12096
Lehmann, J., Goussios, C., & Seufert, T. (2016). Working memory capacity and disfluency effect: An aptitude-treatment-interaction study. Metacognition and Learning, 11(1), 89–105. doi: 10.1007/s11409-015-9149-z
Liston, C., McEwen, B. S., & Casey, B. J. (2009). Psychosocial stress reversibly disrupts prefrontal processing and attentional control. Proceedings of the National Academy of Sciences, 106(3), 912–917. doi: 10.1073/pnas.0807041106
Melby-Lervåg, M., & Hulme, C. (2016). There is no convincing evidence that working memory training is effective: A reply to Au et al. (2014) and Karbach and Verhaeghen (2014). Psychonomic Bulletin & Review, 23(1), 324–330. doi: 10.3758/s13423-015-0862-z
Melby-Lervåg, M., Redick, T. S., & Hulme, C. (2016). Working memory training does not improve performance on measures of intelligence or other measures of "far transfer": Evidence from a meta-analytic review. Perspectives on Psychological Science, 11 (4), 512-534. doi: 10.1177/1745691616635612
Mischel, W., Ayduk, O., Berman, M. G., Casey, B. J., Gotlib, I. H., Jonides, J., … Shoda, Y. (2011). “Willpower” over the life span: Decomposing self-regulation. Social Cognitive and Affective Neuroscience, 6(2), 252–256. doi:10.1093/scan/nsq081
Miyake, A., & Friedman, N. P. (2012). The nature and organization of individual differences in executive functions: Four general conclusions. Current Directions in Psychological Science, 21(1), 8–14. doi:10.1177/0963721411429458
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100. doi: 10.1006/ cogp.1999.0734
Oberauer, K., Schulze, R., Wilhelm, O., & Süß, H. M. (2005). Working memory and intelligence—Their correlation and their relation: Comment on Ackerman, Beier, and Boyle (2005). Psychological Bulletin, 131, 61–65. doi: 10.1037/0033-2909.131
Oswald, F. L., McAbee, S. T., Redick, T. S., & Hambrick, D. Z. (2015). The development of a short domain-general measure of working memory capacity. Behavior Research Methods, 47(4), 1343–1355. doi: 10.3758/s13428-014-0543-2
Park, B., Korbach, A., & Brünken, R. (2015). Do learner characteristics moderate the seductive-details-effect? A cognitive-load-study using eye-tracking. Journal of Educational Technology & Society, 18(4), 24–36.
Peng, P., Namkung, J., Barnes, M., & Sun, C. (2016). A meta-analysis of mathematics and working memory: Moderating effects of working memory domain, type of mathematics skill, and sample characteristics. Journal of Educational Psychology, 108(4), 455–473. doi: 10.1037/edu0000079
Pnevmatikos, D., & Trikkaliotis, I. (2013). Intraindividual differences in executive functions during childhood: The role of emotions. Journal of Experimental Child Psychology, 115(2), 245–261. doi: 10.1016/j.jecp.2013.01.010
Primi, R., Ferrão, 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. doi:10.1016/j.lindif.2010.05.001
Redick, T. S., Broadway, J. M., Meier, M. E., Kuriakose, P. S., Unsworth, N., Kane, M. J., & Engle, R. W. (2012). Measuring working memory capacity with automated complex span tasks. European Journal of Psychological Assessment, 28, 164–171. doi: 10.1027/ 1015 5759/a000123
Redick, T. S., Unsworth, N., Kelly, A. J., & Engle, R. W. (2012). Faster, smarter? Working memory capacity and perceptual speed in relation to fluid intelligence. Journal of Cognitive Psychology, 24, 844–854. doi: 10.1080/20445911.2012.704359
Renkl, A. (2014). Toward an instructionally oriented theory of example-based learning. Cognitive Science, 38(1), 1–37. doi:10.1111/cogs.12086
Salden, R. J., Aleven, V. A., Renkl, A., & Schwonke, R. (2009). Worked examples and tutored problem solving: Redundant or synergistic forms of support? Topics in Cognitive Science, 1(1), 203–213.
Schmiedek, F., Hildebrandt, A., Lövdén, M., Wilhelm, O., & Lindenberger, U. (2009). Complex span versus updating tasks of working memory: The gap is not that deep. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(4), 1089–1096. doi: 10.1037/a0015730
Schmiedek, F., Lövdén, M., & Lindenberger, U. (2014). A task is a task is a task: putting complex span, n-back, and other working memory indicators in psychometric context. Frontiers in Psychology, 5. doi: 10.3389/fpsyg.2014.01475
Schnotz, W., & Kürschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19(4), 469–508. doi: 10.1007/s10648-007-9053-4
Schwaighofer, M., Bühner, M., & Fischer, F. (2016). Executive functions as moderators of the worked example effect: When shifting is more important than working memory capacity. Journal of Educational Psychology, 108(7), 982–1000. https://doi.org/10.1037/edu0000115
Schwaighofer, M., Fischer, F., & Bühner, M. (2015). Does working memory training transfer? A meta-analysis including training conditions as moderators. Educational Psychologist, 50(2), 138–166. doi: 10.1080/00461520.2015.1036274
Seufert, T., Schütze, M., & Brünken, R. (2009). Memory characteristics and modality in multimedia learning: An aptitude–treatment–interaction study. Learning and Instruction, 19(1), 28–42. http://doi: 10.1016/j.learninstruc.2008.01.002
Shipstead, Z., Lindsey, D. R. B., Marshall, R. L., & Engle, R. W. (2014). The mechanisms of working memory capacity: Primary memory, secondary memory, and attention control. Journal of Memory and Language, 72, 116–141. doi: 10.1016/j.jml.2014.01.004
Snow, R. E., & Lohman, D. (1984). Toward a theory of cognitive aptitude for learning from instruction. Journal of Educational Psychology, 76, 347–376.
Sosic-Vasic, Z., Keis, O., Lau, M., Spitzer, M., & Streb, J. (2015). The impact of motivation and teachers’autonomy support on children’s executive functions. Frontiers in Psychology, 6. doi: 10.3389/fpsyg.2015.00146
Spiro, R. J., Coulson, R. L., Feltovich, P. J., & Anderson, D. K. (1988). Cognitive flexibility theory: Advanced knowledge acquisition in illstructured domains. In Proceedings of the Tenth Annual Conference of the Cognitive Science Society (pp. 375–383). Hillsdale, NJ: Erlbaum.
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. doi: 10.1007/s10648-010-9128-5
Sweller, J. (2011). Cognitive load theory. In J. Mestre, & B. Ross (Eds.), The psychology of learning and motivation: Cognition in education (Vol. 55, pp. 37 – 76). Oxford: Academic Press.
Sweller, J., Mawer, R. F., & Howe, W. (1982). Consequences of history-cued and means–end strategies in problem solving. American Journal of Psychology, 95, 455–483. doi: 10.2307/1422136
Trezise, K., & Reeve, R. A. (2014). Cognition-emotion interactions: patterns of change and implications for math problem solving. Frontiers in Psychology, 5. doi: 10.3389/fpsyg.2014.00840
Tsaparlis, G. (2005). Non-algorithmic quantitative problem solving in university physical chemistry: A correlation study of the role of selective cognitive factors. Research in Science & Technological Education, 23, 125–148. doi: 10.1080/02635140500266369
Tun, P. A., Miller-Martinez, D., Lachman, M. E., & Seeman, T. (2013). Social strain and executive function across the lifespan: The dark (and light) sides of social engagement. Aging, Neuropsychology, and Cognition, 20(3), 320–338. doi: 10.1080/13825585.2012.707173
van Gog, T., & Rummel, N. (2010). Example-based learning: Integrating cognitive and social-cognitive research perspectives. Educational Psychology Review, 22(2), 155–174. doi:10.1007/s10648-010-9134-7
van Merriënboer, J. J. G., & Kirschner, P. A. (2012). Ten steps to complex learning: A systematic approach to four-component instructional design (2nd ed.). New York: Routledge.
Wilhelm, O., Hildebrandt, A., & Oberauer, K. (2013). What is working memory capacity, and how can we measure it? Frontiers in Psychology, 4. doi: 10.3389/fpsyg.2013.00433
Yeniad, N., Malda, M., Mesman, J., van Ijzendoorn, M. H., & Pieper, S. (2013). Shifting ability predicts math and reading performance in children: A meta-analytical study. Learning and Individual Differences, 23, 1–9. doi:10.1016/j.lindif.2012.10.004