Friday, August 5, 2011

Remarks on human nature and the implications for learning and instruction

As Wittgenstein said in the Tractatus (2.1), we picture facts to ourselves. We create internal representations to understand that which we experience. These internal representations can be considered a kind of model of reality (in some cases, of non-reality, as some people sometimes also picture non-facts to themselves … they are called politicians). We continuously create these internal models, most often without thought and with little conscious effort. It is what we do … it is part of being a person. We are all mental modelers … one and all.

A model is necessarily a simplification of that which is modeled. Our internal representations do not have all of the detail and complexity of the situation or experience that is being modeled. Internal models could not possibly contain all of the detail of the external reality that is being modeled. Another way of saying this is that we simplify reality in order to understand it. This is not an intention or plan or strategy on our part … it is built into the nature of perception, conception, and coming to know and understand our worlds. We are all simplifiers … one and all.

There are many advantages to and even an evolutionary explanation for this ability to create internal representations and simplify external realities (see Sweller, 2003, for more on this). However, a tension arises, particularly for educators, academics, researchers, and those working with very complex, dynamic and ill-structured problem situations. The tension is to help a person avoid oversimiplifying and focus on key aspects of the problem situation in order to respond appropriately. The need to address this tension can be found in Dietrich Dörner’s (1996) The Logic of Failure: Why Things Go Wrong and What We Can Do To Make Them Right. Balancing our natural simplilfication processes (constructing internal mental representations) with the complexity of actual problems is a challenge for educators in many different disciplines.

An instructional design approach that addresses this tension is called model-faciliated learning (Milrad, Spector, & Davidsen, 2003). MFL suggests three stages of learning and developing complex problem solving skill and expertise:

  1. problem-orientation (problem confronting and problem solving), in which learners are presented typical problem situations and asked to solve relatively simply problems;
  2. inquiry-exploration (hypothesis formulation and experimentation), in which learners are challenged to explore a complex domain and asked to identify and elaborate causal relationships and dominant underlying structures; and,
  3. policy-development (decision making rule and global system elaboration), in which learners are immersed in the full complex system and asked to develop rules and heuristics to guide decision making in order to create stability or avoid undesirable situations.
The MFL principle of graduated complexity suggests a learning sequence such as the following:
  1. Challenge learners to characterize the expected behavior of a complex system (how the system typically behaves with an indication of how components are interrelated).
  2. Challenge learners to identify key variables and leverage points with respect to a desired outcome. 
  3. Challenge learners to identify and explain likely causes for observed system behavior, especially in terms of key factors that might be subject to control and manipulation.. 
  4. Challenge learners to reflect on dynamic aspects of the system in the context of decision making and policy guides to achieve desired outcomes.
  5. Challenge learners to encapsulate learning in terms of a rationale for system structure, decision-making guidelines, and an elaborated strategy for policy formulation.
  6. Challenge learners to diversify and generalize to new problem situations. (To assess deep understanding one might ask learners to create a dynamic model relevant to an apparently new problem situation that is likely to have an underlying structure similar to a problem situation already resolved by the learner).
    We create internal representations, we simplify, and we occasionally try to understand the complexity of our worlds … it is who we are.


    Dörner, D. (1996) (Translated by Rita and Robert Kimber). The logic of failure: Why things go wrong and what we can do to make them right. New York: Holt.
    Milrad, M., Spector, J. M., & Davidsen, P. I. (2003). Model facilitated learning. In S. Naidu (Ed.), Learning and Teaching with Technology: Principles and Practices (pp. 13-27). London: Kogan Page.
    Sweller, J. (2003). Evolution of human cognitive architecture. In B. Ross (Ed.), The psychology of learning and motivation (Vol. 43, pp. 215-266). San Diego: Academic Press.