Edev_502 wk2_1

This week our focus was on the knowledge of the expert. What comprises expert knowledge and how does knowing about how they know help educationalist? I had difficulty believing that experts could not express their knowledge even though a body of theorising has developed making that claim. In this essay and in the follow-up posts, I investigate this claim more.


The epistemology of instructional design suffers from incommensurability due to the lack of a unified theory of education. Darwin and Newton unify biology and physics in the hard sciences respectively, yet the social sciences are “snarled by disunity and a failure of vision” (Wilson, 1999, p. 198). Transcending this perceived “atmosphere of chaos” (ibid.), Wilson argues for consilience, the unification of all branches of knowledge. However, it is arguable that such a unity is possible. Margolis and Laurence (2007) point out that the productivity in natural languages allows for billions of times more thoughts than there are neurons in the brain.1 The question, therefore, about grammar of thought residing in the emergence of relationships between physical units or a in a separate physical unit by itself is not problematic. Schneider (2011) sees the mind as a store of symbols “that combine into mental sentences according to the grammatical principles of the language” (p. 6-7). Fodor’s Language of Thought answers questions of how multitudes of thoughts and how systematic meanings are possible (Fodor, 1975, in Schneider, 2011). This theoretical background suggests to educators to investigate conceptual areas and discern their components. Instructional designers can understand expertise and expert performance2 (Ericsson, 2006, p. 4) in terms of systems of thought that offer a solid theory in which to position educational epistemologies.

In this essay, I demonstrate how curriculum developers may present expert knowledge usefully as a pedagogic device using a uni-directional model going from discrete components to combined concepts. Anderson and Schunn (2000) present the unequivocal position that “each piece of knowledge requires its own due of learning” and “that there is no ‘free lunch’” (p. 2). Each element of procedural ‘know how’ and declarative ‘know that’ (Dean, 2006; Gagne, 1984; Hofer & Pintrich, 1997) must be isolated and each component part understood. How the expert chunks (Bangert, Schubert, & Fabian, 2014; Feigenbaum & Simon, 1984) the elements into progressively larger entities is described. Marton and Pang (2006) demonstrate how this is done using the economic concept of opportunity cost. Chunks organise into schemas (Charlin, Boshuizen, Custers, & Feltovich, 2007). Charlin and his associates label these as ‘scripts’ which they use to teach medical students.

Dreyfus and Dreyfus (2005) and Rolfe (1997) claim that experts themselves cannot be relied upon as sources of this information because they have “an inability to explain expertise” (Rolfe, 1997, p. 1070) and ascribe their skill to intuition. However, Toner, Montero and Evans ( 2014) challenge this view arguing that intuition (at least in chess) is “rational through and through” (p. 175). In my view, advanced learning does not forget lower level information (i.e. absorption into intuition) but is complex schemata building. Advanced (music) performers chunk smaller units into bigger ones (Bangert et al., 2014). Larger patterns emerge as schema. Chunking aids proficiency because the larger the chunk the less direct cognitive engagement is required, freeing up the attention for other aspects of performance. Expertise is not only a matter of the amount of propositional knowledge acquired but “having it better organised and more readily available for use” (Eraut, 1994, p. 129). This ability has also been observed in chess masters who have functionally proceduralised over 50,000 board positions (Dreyfus & Dreyfus, 2005). An argument can be made that the reason for the failure of an educational programme may the paucity of input if the number of elements presented to learners falls short.

In this essay, I have placed a discrete element view of instructional design inside the theory of symbol processing using a dissection of expertise into its component parts. This view complements the common practice of selecting and sequencing elements to learners that are teachable and learnable at given levels of proficiency. A fuller description would include an explanation of how non-propositional expertise is developed and how threshold concepts (Fry, 2009) serve to answer questions about how teachers support learners make cognitive leaps over conceptually problematic issues instead of “relying on the [notion of] community of practice to provide what we can’t get at” (Atherton, 2013).

Footnotes

[1] This argument does not go far enough. With just 4 neurons, 24 neural connections are possible, i.e. 4 x 3 x 2 x 1. With a US billion neurons, the number of possible connections (10 followed by 250 million zeros) exceeds by far all atoms in the known universe. As neural connections consist of atoms, connections cannot be physical objects. Consilience, therefore, is a logical contradiction as it is physically impossible to describe thought in an internalist framework, but potentially powerful theories such as connectivism must rely on representational emergence.

[2] I focus only on Ericsson’s definitions to bypass a critical difference in the representation of expertness between Dreyfus and Dreyfus (2005) and Schön (1992) that disagree over the expert’s ability to describe their action. For the Dreyfuses, experts have difficulty articulating past actions (p. 788); Schön sees reflection on past action as a key mechanism for professional development.

References

Anderson, J. R., & Schunn, C. D. (2000). Implications of the ACT-R learning theory: No magic bullets. Advances in Instructional Psychology (Vol. 5), 5, 1–34.

Atherton J S (2013) Doceo; Introduction to Threshold Concepts [On-line: UK] retrieved 18 July 2015 from http://www.doceo.co.uk/tools/threshold_3.htm

Bangert, D., Schubert, E., & Fabian, D. (2014). A spiral model of musical decision-making. Frontiers in Psychology, 5(April), 320. doi:10.3389/fpsyg.2014.00320

Charlin, B., Boshuizen, H. P. a, Custers, E. J., & Feltovich, P. J. (2007). Scripts and clinical reasoning. Medical Education, 41(12), 1178–1184. doi:10.1111/j.1365-2923.2007.02924.x

Dean, C. (2006). Authenticity, Virtue, Expertise: Ethical being and becoming ethical. International Journal of the Humanities, 3(3).

Dreyfus, H. L., & Dreyfus, S. E. (2005). Expertise in real world contexts. Organization Studies, 26(5), 779–792. doi:10.1177/0170840605053102

Eden, A. H., Moor, J. H., Søraker, J. H., & Steinhart, E. (n.d.). Singularity hypotheses: An overview. In A. H. Eden, J. H. Moor, J. H. Søraker, & E. Steinhart (Eds.), Singularity hypotheses: A scientific and philosophical assessment (pp. 1–15). Heidelberg: Springer.

Eraut, M. (1994). “Theories of Professional Expertise.” Developing Professional Knowledge and Competence.

Ericsson, A. K. (2006). An introduction to Cambridge handbook of expertise and expert Performance. In N. Charness & R. R. Hoffman (Eds.), Cambridge handbook of expertise and expert Performance. West Nyack, NY, USA: Cambridge University Press.

Feigenbaum, E. a, & Simon, H. a. (1984). EPAM-like models of recognition and learning. Cognitive Science, 8, 305–336. doi:10.1016/S0364-0213(84)80005-1

Gagne, R. M. (1984). Learning outcomes and their effects: Useful categories of human performance. American Psychologist, 39(4), 377–385. doi:10.1037/0003-066X.39.4.377

Hofer, B. K., & Pintrich, P. R. (1997). The Development of Epistemological Theories: Beliefs About Knowledge and Knowing and Their Relation to Learning. Review of Educational Research, 67(1), 88–140. doi:10.3102/00346543067001088

Margolis, E., & Laurence, S. (2007). The Ontology of Concepts—Abstract Objects or Mental Representations? Nous, 41(4), 561–593. doi:10.1111/j.1468-0068.2007.00663.x

Marton, F., & Pang, M. F. (2006). On Some Necessary Conditions of Learning. Journal of the Learning Sciences, 15(2), 37–41. doi:10.1207/s15327809jls1502

Rolfe, G. (1997). Science, abduction and the fuzzy nurse: an exploration of expertise. Journal of Advanced Nursing, 25(5), 1070–1075. doi:10.1046/j.1365-2648.1997.19970251070.x

Schneider, S. (2011). Language of thought : A new philosophical direction (pp. 1–25). Cambridge, MA, USA: MIT Press.

Schön, D. A. (1992). The crisis of professional knowledge and the pursuit of an epistemology of practice. Journal of Interprofessional Care, 6(1), 49–63. doi:10.3109/13561829209049595

Toner, J., Montero, B. G., & Moran, A. (2014). Considering the role of cognitive control in expert performance. Phenomenology and the Cognitive Sciences. doi:10.1007/s11097-014-9407-6

Wilson, E. O. (1999). Consilience: The unity of knowledge. New York: Vintage Books. doi:10.1038/143391a0

About theCaledonian

Scot living in north Japan teaching at a national university.
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