It’s Safe to Come Back Now.
Essentially, early applications of AI to model the brain and human learning failed because it viewed cognition as internal and sequestered from the environment. Constructivism and Situated Learning theories filled the gap, exploring best practices from a broader learning environment perspective. Their success as theories seem to permeate the MET program. After significant developments in neuroscience, it’s safe to come back to cognitive learning theories!
Can the Brain Operate in the Absence of an Environment?
Embodied cognition seems to come down to this question. In the old system of AI, although not necessary, once “loaded with programs” the brain could operate independently of its environment, a computer floating through space just doing its own thing. The key change was to overthrow the “isolated brain” model and replace it with a complex, adaptive cognition system that is floating in an environmental soup. In this model the “computer” cannot operate without a context. Moreover, the embedded connection between the corporeal organs of the cognitive system (eyes, ears, etc) and the environment form a unique “umwelt”. In other literature, I’ve heard this called a “lifeworld”. A paper by Jones (2013) notes that students are naturally motivated to learn and develop successful adaptations to their environment when involved in informal learning activities, like Geocaching. I believe motivation and the concept of umwelt are very strongly connected. That is, it is easier to be motivated to learn things when you perceive them clearly and see subtleties in the same way that “beer tasters…[have]… heightened perceptual discrimination” (Winn, 2003, p. 13). In his 2010 article, Nunez argues that the time is right to develop and use a more rigorous scientific approach to this theory of learning.
Learning as Adaptation.
This section of the Winn paper produces a teaching “road map” of sorts for providing the desired environmental pressures to idealize learning. If bio-chemistry and genetic history provide a basis for our cognition, then environment provides the pressure to adapt or “learn” in stages:
- Notice something is wrong with concept. (Declare a break)
- Disambiguate the effect. (Draw a distinction)
- Embed the “new rule” to the existing conceptual network. (Ground the distinction)
- Give the idea a trial run to test its usefulness. (Embodying the distinction)
This seems a lot like Scaffolded Knowledge Integration with an additional “usefulness testing” stage. These readings have made me more aware of the situated learning in my own practce as it relates to the senses. I can see that the design and building of physical artifacts in PBL is of crucial importance!
Questions for Colleagues
- There is a mention of “Genetic predisposition to change” (Winn, 2002, p. 19). Does this suggest that some students are genetically better at learning?
- Further in the paper, Winn states “The rules or procedures, that specify how the student interacts with the environment in the first place also change through adaptation, based on their success at producing fruitful behaviour.” (Winn, 2002, p. 20). Is this the same as saying that winning begets winning? Is learning exponential or self-rewarding?
- Finally, in reference to Jones’ (2013) study of informal learning structures, how do we leverage the intrinsic motivational features of informal learning and make it count for our more formal processes? Can understanding student “umwelt” and making their learning visible help us chose more motivating projects and approaches to teaching?
Jones, A., Scanlon, E., & Clough, G. (2013). Mobile learning: Two case studies of supporting inquiry learning in informal and semiformal settings. Computers & Education 61, 21-32.
Linn, M., Clark, D., & Slotta, J. (2003). Wise design for knowledge integration. Science Education, 87(4), 517-538.
Núñez, R. (2012). On the science of embodied cognition in the 2010s: Research questions, appropriate reductionism, and testable explanations. Journal of the Learning Sciences, 21(2), 324-336.
Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114.