Cognition is embodied in physical activity interacting within contextual situations, constructing knowledge instead of traditional encoding, remembering and recalling. Learning incorporates cerebral with bodily, negotiating meaning through social activity externalizing thinking as behavior. Reciprocal interaction between mind and environment offers differing perspectives, for example interpreting 3D space using bodies as data points. Our brains map objects to representations, where mental reasoning depends on factors like economic status, ethnicity, family support, teacher quality and school preparation (Winn, 2003). Biological adaptation transforms pictures into associative networks dynamically interacting sensory inputs. Bodily engagement like thought-gesture coproduction grounds perception and action with physical environment (Núñez, 2012), where experience develops structure towards higher level conceptual systems. Reality environment can differ from known umwelt (Winn, 2003) based on individual background, where multiple ways of knowing removes fixed objective standards to assess knowledge. Thinking is embodied and situated, testing hypotheses and explanations, where science uses methodical experimentation collecting empirical data to avoid reductionism. Science operationalizes cognitive mechanisms, using speech, gesture, eye and thought synchronization to make introspections of observations.
Technology reduces physical limits enabling metaphorical representations out of reach from direct experience, instrumenting tools to observe phenomena and make inferences. Explanations are limited by natural environment viewing from particular time space scales. Metaphors however risk misinterpretation or incomplete understanding, for example distorting time whose simplification results in misconception (Winn, 2003). Complete attention to immersion may produce flow as total engagement and enjoyment losing track of time, while reduced presence caused by distraction or discomfort impedes learning. Aleahmad and Slotta (2002) used handheld technologies with browser-based learning environments to scaffold data collection and reflection activities. WISE Inquiry maps coordinated activities, embedding pop-up notes or hints. Convenient portable data creation makes content dynamic with syncing, where beaming checklists have ‘cool’ factor maximizing take-home learning through social networks. Learners select criteria for wider audiences, evaluating sources when synthesizing observations.
During teacher education, a particular lesson involved volunteers lining up holding papers containing numbers and a decimal point. As students reorganized themselves along the line, they embodied significant figures in Chemistry. However the extent of learning content between observers and participants may vary, having difficulty translating activity back into traditional media. Educators persuade students to challenge uncertainty with difficulty embedded in constructive activity towards intuitive explanations. Optimal challenge elicits curiosity to adapt embodied interactions to affect deeply-rooted belief and genetic predisposition to change. Instruction proves effective only when employed at appropriate levels of granularity. Embeddedness employs interdependence between cognition and environment, dynamically interacting to regularly process learning.
- How do we coordinate movement towards cognition for learning content? What is the bodily basis of conception, image schemas and conceptual mappings (Núñez, 2012)?
- What affordances and limitations does each representation mode provide? What specific advantages for example does real-world data have over dynamic content?
Aleahmad, T. & Slotta, J. (2002). Integrating handheld Technology and web-based science activities: New educational opportunities. Paper presented at ED-MEDIA 2002 World Conference on Educational Multimedia, Hypermedia & Telecommunications. Proceedings (14th, Denver, Colorado, June 24-29, 2002); see IR 021 687.
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.