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to gather data to justify our hypothesis. We have conducted several pilot research projects, which appear to support our hypothesis, and we will continue to conduct research in this area.


Advancing Technology—Developing External Representations

We have only begun to explore what are the appropriate scaffolds for promoting learning. We have also much to learn on how computational and communication technologies can support teacher collaboration and professional development .

- Eliot Soloway, Scaffolding Technology Tools to Promote

Teaching and Learning in Science

The above model is inspired by theory often used to describe complex interactions in engineering systems, and as such is not intended to explain how learning works,  but rather is intended to give us a framework for thinking about and posing questions about the role of emotions in learning.  As with any metaphor, the model has limitations to its application. In this case, the model is not intended to fully describe all aspects of the complex interaction between emotions and learning, but rather only to serve as a beginning for describing some of the key phenomena that we think are all too often overlooked in learning pedagogy. This model goes beyond previous research studies not just in the emotions addressed, but also in an attempt to formalize an analytical model that describes the dynamics of emotional states during model-based learning experiences, and to do so in a language that the SMET learner can come to understand and utilize.

External representations can fulfill a number of roles in artificial intelligence systems. But the external representations need to be sensitive to the affective state of the learner, which varies through the learning journey, and, in large measure, influences how efficiently and effective a learner acquires and processes information/knowledge. Based upon an understanding of this model, the structure and function of external representations would vary according to a learner’s affective state as opposed to the assumption that one-size-fits-all.

4. References

Bansford, John, Ann L. Brown, and Rodney Cocking (Eds.) (1999). How People Learn: Brain, Mind, Experience, and School. Washington DC: National Academy Press.

Bruer, J.T. (1993). Schools for Thought. Cambridge, Mass.: MIT Press.

Cobb, B. (1994). “Theories of Mathematical Learning and Constructivism: A Personal View.” Paper presented at the Symposium on Trends and Perspectives in Mathematics Education, Institute for Mathematics, University of Klagenfurt, Austria.

Chen, L.S., T.S. Huang, T. Miyasato, and R. Nakatsu, “Multimodal human emotion/expression recognition,” in Proc. of Int. Conf. on Automatic Face and Gesture Recognition, (Nara, Japan), IEEE Computer Soc., April 1998.

Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience, Harper-Row: NY.

DeSilva, L.C., T. Miyasato, and R. Nakatsu, Facial emotion recognition using multi-modal information, in Proc. IEEE Int. Conf. on Info., Comm. and Sig. Proc., (Singapore), pp. 397-401, Sept 1997.

Donato, G., M.S. Bartlett, J.C. Hager, P. Ekman, and T.J. Sejnowski,  Classifying facial actions, IEEE Pattern  Analy. and Mach. Intell., vol. 21, pp. 974--989, October 1999.

Ekman, Paul. (1992). Are there basic emotions?, Psychological Review, 99(3): 550-553.

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