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5 - Understanding mind, brain, and education as a complex, dynamic developing system: Measurement, modeling, and research

from Part I - The mind, brain, and education triad

Published online by Cambridge University Press:  22 September 2009

Paul Van Geert
Affiliation:
Faculty of Behavioral and Social Sciences Department of Psychology University of Groningen
Henderien Steenbeek
Affiliation:
Faculty of Behavioral and Social Sciences Department of Psychology University of Groningen
Antonio M. Battro
Affiliation:
National Academy of Education, Argentina
Kurt W. Fischer
Affiliation:
Harvard University, Massachusetts
Pierre J. Léna
Affiliation:
Université de Paris VII (Denis Diderot)
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Summary

Overview

Human development and education can benefit from a framework that analyzes behavior and brain change as involving dynamic systems processes. Dynamic systems researchers build specific models focusing on processes of change in learning and teaching, beginning with individual growth patterns and including in mathematical models multiple layers and scales of casual interaction. These models shift the focus of research and assessment to individual behavior, fluctuations in time, and the combination of gradual change with periodic abrupt changes in performance and brain patterns. Dynamic systems models explain and predict important properties of learning and teaching such as non-linear change and self-organization (spontaneous increase of order and information). They readily combine apparently opposite processes in the same theory and model, such as gene versus environment or individual versus context/culture, a characteristic called superposition. Measurements should involve the kind of assessment that teachers and schools do every day in the classroom – repeated measures of individual behavior. The models then provide ways of analyzing common educational phenomena, such as variability in performance, ambiguity of behavior, and context specificity. A dynamic approach promises to provide useful tools for understanding the complex individual changes that occur during education and child development.

The Editors

Human development constitutes a complex system. Rocha (1999) defines a complex system as “… any system featuring a large number of interacting components (agents, processes, etc.) … whose aggregate activity is nonlinear (not derivable from the summations of the activity of individual components) … and typically exhibits … self-organization …”.

Type
Chapter
Information
The Educated Brain
Essays in Neuroeducation
, pp. 71 - 94
Publisher: Cambridge University Press
Print publication year: 2008

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References

Alibali, M. (1999). How children change their minds: strategy change can be gradual or abrupt. Developmental Psychology, 35(1), 127–145.CrossRefGoogle ScholarPubMed
Bassano, D. and , Geert P. (2007). Modeling continuity and discontinuity in utterance length: a quantitative approach to changes, transitions and intra-individual variability in early grammatical development. Developmental Science, 10(5), 588–612.CrossRefGoogle ScholarPubMed
Beaulieu, A. (2000). The space inside the skull: digital representations, brain mapping and cognitive neuroscience in the decade of the brain. Groningen: Doctoral Dissertation.Google Scholar
Black, B. and Logan, A. (1995). Links between communication of mother-child, father-child, and child-child peer interactions and children's social status. Child Development, 66, 255–271.CrossRefGoogle Scholar
Butler, A., Hokanson, J., and Flynn, H. A. (2004). A comparison of self-esteem lability and low trait self-esteem as vulnerability factors for depression. Journal of Personality and Social Psychology, 66, 166–177.CrossRefGoogle Scholar
Campos, J. J., Mumme, D. L., Kermoian, R., and Campos, R. G. (1994). A functionalist perspective on the nature of emotion. Monographs of the Society for the Study of Child Development, 59 (2–3), 284–303.CrossRefGoogle ScholarPubMed
Clark, A. (1997). Being There: Putting Brain, Body and World Together Again. Cambridge, MA: MIT Press.Google Scholar
De, Koeijer I. (2001). Peer Acceptance, Parent-child Fantasy Play Interactions, and Subjective Experience of the Self-in-relation; A Study of 4- to 5-year-old Children. Veenendaal: Universal Press.Google Scholar
Weerth, C. and , Geert -P. (2002). Changing patterns of infant behavior and mother-infant interaction: Intra- and interindividual variability. Infant Behavior and Development. 24(4), 347–371.CrossRefGoogle Scholar
De, Weerth C. and , Geert P. L. C. (2002). A longitudinal study of basal cortisol in infants: intra-individual variability, circadian rhythm and developmental trends. Infant Behavior and Development, 25, 340–374.Google Scholar
Weerth, C., , Geert P., and Hoijtink, H. (1999). Intraindividual variability in infant behavior. Developmental Psychology, 35 (4), 1102–1112.CrossRefGoogle ScholarPubMed
Eizenman, D. R., Nesselroade, J. R., Featherman, D. L., and Rowe, J. W. (2004). Intra-individual variability in perceived control in an older sample: the macArthur Successful Aging Studies. Psychology and Aging, 12, 489–502.CrossRefGoogle Scholar
Fischer, K. W. and Thomas R. Bidell (2006). Dynamic development of action, thought and emotion. In Lerner, R. M. and Damon, W. W. (eds.), Handbook of Child Psychology. Vol 1: Theoretical Models of Human Development (6 edn pp. 313–399). New York: Wiley.Google Scholar
Fischer, K. W., Bullock, D. H., Rotenberg, E. J., and Raya, P. (1993). The dynamics of competence: how context contributes directly to skill. In Wozniak, R. H. and Fischer, K. W. (eds.), Development in Context: Acting and Thinking in Specific Environments. Hillsdale, NJ: Erlbaum, pp. 93–117.Google Scholar
Ford, D. and Lerner, R. (1992). Developmental Systems Theory: An Integrative Approach. London: Sage.Google Scholar
Frijda, N. H. (1986). The Emotions: Studies in Emotion and Social Interaction. Cambridge: Cambridge University Press.Google Scholar
Goldin-Meadow, S., Alibali, M. W., and Breckinridge, Church R. (1993). Transitions in concept acquisition: Using the hand to read the mind. Psychological Review, 100(2), 279–297.CrossRefGoogle ScholarPubMed
Good, P. I. (1999). Resampling Methods: A Practical Guide to Data Analysis. Boston: Birkhauser.CrossRefGoogle Scholar
Gottlieb, G., Wahlsten, D., and Lickliter, R. (1998). The significance of biology for human development: a developmental psychobiological systems view. In Damon, W. & Lerner, R. (eds.), Handbook of Child Psychology (pp. 233–273). New York: Wiley.Google Scholar
Granic, I., Hollenstein, T., Dishion, Th. J., and Patterson, G. R. (2003). Longitudinal analysis of flexibility and reorganization in early adolescence: A dynamic systems study of family interactions. Developmental Psychology. 39(3):, 606–617.CrossRefGoogle ScholarPubMed
Hosenfeld, B., Maas, H. L. J. Boom, D. C. (1997). Indicators of discontinuous change in the development of analogical reasoning. Journal of Experimental Child Psychology, 64, 367–395.CrossRefGoogle ScholarPubMed
Jansen, B. R. J. and der, Maas H. L. J. (2002). The development of children's rule use on the balance scale task. Journal of Experimental Child Psychology, 81, 383–416.CrossRefGoogle ScholarPubMed
Kernis, M. H., Cornell, D., Sun, C.-R., Berry, A., and Harlow, T. (1993). There's more to self-esteem than whether it is high or low: the importance of stability of self-esteem. Journal of Personality and Social Psychology, 65, 1190–1204.CrossRefGoogle ScholarPubMed
Li, S.-C., Lindernberger, U., Hommel, B., Aschersleben, G., Prinz, W., and Baltes, P. (2004). Lifespan transformations in the couplings among intellectual abilities and constituent cognitive processes. Psychological Science, 15(3), 155–163.CrossRefGoogle ScholarPubMed
Li, S.-C., Aggen, S. H., Nesselroade, J. R., and Baltes, P. B. (2001). Short-term fluctuations in elderly people's sensori-motor functioning predict text and spatial memory performance: the MacArthur successful aging studies. Journal of Gerontology, 47, 100–116.CrossRefGoogle Scholar
Manly, B. F. (1997). Randomization, Bootstrap and Monte Carlo Methods in Biology (2nd edition). Boca Raton: Chapman and Hall.Google Scholar
Maassen, G. H., Akkermans, W. and Linden, J. L. (1996). Two-dimensional sociometric status determination with rating scales. Small Group Research, 27(1), 56–78.CrossRefGoogle Scholar
Mazoyer, B. and Tzouriou-Mazoyer, N. (2002). Variabilité anatomique et fonctionelle des aires du langage. In Lautrey, J., Mazoyer, B., and Geert, P. (eds.), Invariants et variabilités dans les sciences cognitives (pp. 55–68). Paris: Editions de la Maison des Sciences de l'Homme.CrossRefGoogle Scholar
Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: bringing the person back into scientific psychology – this time forever. Measurement, 2 (4), 201–219.Google Scholar
Musher-Eizenman, D. R., Nesselroade, J. R., and Schmitz, B. (2002). Perceived control and academic performance: a comparison of high- and low-performing children on within-person change-patterns. International Journal of Behavioral Development, 26, 540–547.CrossRefGoogle Scholar
Rabbitt, P., Osman, P., and Moore, B. (2001). There are stable individual differences in performance variability, both from moment to moment and from day to day. The Quarterly Journal of Experimental Psychology, 54A, 981–1003.CrossRefGoogle Scholar
Rocha, L. M. (1997). Evidence Sets and Contextual Genetic Algorithms: Exploring Uncertainty, Context, and Embodiment in Cognitive and Biological Systems. New York: Binghampton University Doctoral dissertation.Google Scholar
Roubertoux P. L. and Carlier, M. Invariants et variants génetiques: les apports de la génomique dans l'étude des processus cognitifs. In Lautrey, J., Mazoyer, B., and Geert, P. (eds.), Invariants et variabilités dans les sciences cognitives. (pp. 25–40). Paris: Editions de la Maison des Sciences de l'Homme.CrossRef
Rubin, K. H., Bukowski, W. M., and Parker, J. G. (1998). Peer interactions, relationships, and groups. In Damon, W. (Series ed.) and Eisenberg, N. (Vol. ed.), Handbook of Child Psychology: Vol. 3. Social, Emotional, and Personality Development (5th edn., pp. 619–700). New York: Wiley.Google Scholar
Schmitz, B. and Skinner, E. (1993). Perceived control, effort and academic performance: interindividual, intraindividual en multivariate time-series analyses. Journal of Personality and Social Psychology, 64, 1010–1028.CrossRefGoogle Scholar
Steenbeek, H. and Geert, P. (2002). Variations on dynamic variations. Human-Development. May–Jun; Vol. 45(3), 167–173.CrossRefGoogle Scholar
Steenbeek, H. and , Geert P. (2005). A dynamic systems model of dyadic interaction during play of two children. European Journal of Developmental Psychology, 2(2), 105–145.CrossRefGoogle Scholar
Steenbeek, H. and , Geert P. (2007a). A dynamic systems approach to dyadic interaction in children's emotional expression, action, dyadic play, and sociometric status. Developmental Review, 27(1), 1–40.CrossRefGoogle Scholar
Steenbeek, H. and , Geert P. (2007b). The empirical validation of a dynamic systems model of interaction: do children of different sociometric status differ in their dyadic play interactions? Developmental Science (in press).Google Scholar
Thelen, E. and Smith, L. B. (1994). A Dynamic Systems Approach to the Development of Cognition and Action, Cambridge, MA: MIT Press.Google Scholar
Todman, J. B. and Dugard, P. (2001). Single-case and Small-n Experimental Designs: A Practical Guide to Randomization Tests. Mahwah, NJ: Erlbaum.Google Scholar
Uttal, W. M. (2004). The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain. Cambridge, MA: Cambridge University Press.Google Scholar
der, Maas H. (1993). Catastrophe analysis of stagewise cognitive development, model method and applications. Dissertation, University of Amsterdam.Google Scholar
der, Maas H. and Molenaar, P. (1992). A catastrophe-theoretical approach to cognitive development. Psychological Review, 99, 395–417.Google Scholar
, Dijk M. and , Geert P. (2005). Disentangling behavior in early child development: Interpretability of early child language and its effect on utterance length measures. Infant Behavior and Development, 28, 99–117.CrossRefGoogle Scholar
, Geert P. and , Dijk M. (2002). Focus on variability: New tools to study intra-individual variability in developmental data. Infant Behavior and Development, 25, 1–35.Google Scholar
, Geert P. and , Dijk M.(2003). Ambiguity in child language. The problem of inter-observer reliability in ambiguous observation data. First Language, 23(3), 259–284.CrossRefGoogle Scholar
, Geert P. (1991). A dynamic systems model of cognitive and language growth. Psychological Review, 98, 3–53.CrossRefGoogle Scholar
, Geert P.(1994). Dynamic Systems of Development. New York and London: Harvester Wheatsheaf.Google Scholar
, Geert P.(1998). A dynamic systems model of basic developmental mechanisms: Piaget, Vygotsky and beyond. Psychological Review, 105, 5, (4), 634–677.Google Scholar
Van Geert, P.(2002). Developmental dynamics, intentional action and fuzzy sets. In Granott, N. and Parziale, J. (eds.), Microdevelopmental Clues: Transition Processes in Development and Learning, (pp. 319–343), Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Vygotsky, L. S. (1978). Mind in Society. Londen: Harvard University Press.Google Scholar
Wimmers, R. H. (1996). Grasping Developmental Change: Theory, Methodology and Data. Doctoral Dissertation: Free University of Amsterdam.Google Scholar

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