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briefly review the metagoals of education/learning in our previous history.

In Colonial days schools were based upon ‘recitation literacy’ and from the World War I era forward schools were based upon ‘extraction literacy’ [Wolf, 1988]. However a major shift in the intellectual abilities has recently heightened the need for students of the new millennium to understand their state of their knowledge, to build upon it, to improve it, and to apply it appropriately. In short “[s]ociety envisions graduates of school systems who can identify and solve problems and make contributions to society through their lifetime—who display the qualities of ‘adaptive expertise’” [Bransford, 1999; also see Talbert and McLaughlin, 1993].  Thus contemporary theories view learning as a person’s ability to construct new knowledge based upon what they already know or believe to be true (e.g., Cobb, 1994; Piaget 1952, 1973a, b, 1977, 1978; Vygotsky 1962, 1978). In short, the ability to perform model-based reasoning, recursion, and cognitive assessment (metacognition) are critical elements in contemporary educational pedagogy.

Schools seem to be functioning as well as they ever have, however the challenges and expectations have dramatically changed [e.g., Bruer, 1995; Resnick, 1987]. This educational shift is critical when considering the need to redesign the delivery of education to a learner. These new goals require changes in the design of learning environments. However current learning theory “does not provide a simple recipe for designing effective learning environments” given these changing expectations [Branford, 1999]. “New developments in the science of learning raise important questions about the design of learning environments…[the] general characteristics of learning environments…need to be examined in light of new developments in the science of learning” [Bransford, 1999]. The basis of a model that will serve as a foundation for external representations should be embodied from such a mind-set—developing insightful model-based thinkers. It should recognize the affective and cognitive state of the learner and respond in an appropriate manner (e.g., modulate the pace, direction or complexity of the presentation).

2. Affective State: Emotions and Learning

Do emotions contribute to intelligence, and if so, what are the implications for the development of a technology of affective computing?

- Robert Provine, What Questions Are On Psychologist’s Minds Today?

In an attempt to reengineer the state of educational pedagogy, we should first look to expert teachers who are very adept at recognizing the emotional state of learners and, based upon their observation, taking appropriate action that positively influences learning. But what do these expert teachers see and how do they select a course of action? How do students who have strayed from learning return to a productive path, such as the one that Csikszentmihalyi [1990] refers to as the “zone of flow”?

Skilled humans can assess emotional signals with varying degrees of accuracy, and researchers are beginning to make progress giving computers similar abilities at recognizing affective expressions [e.g., Picard, 2000; Scheirer, et.al., 1999] and from facial expressions [e.g., Donato, 1999; DeSilva, 1997; Ekman, 1997]. Although computers only perform as well as people in highly restricted domains, we believe that accurately identifying a learner’s cognitive-emotive state is a critical indicator that will determine how to assist the learner in appreciating an understanding of the efficiency and pleasure of the learning process.  We also assume that computers will, much sooner than later, be more capable of recognizing human behaviors that afford strong inferences about affective state.

To this end it is necessary for us to rethink our perspective of what is happening during learning and, based upon our hypothesis, reengineer accordingly. This supposition is based upon our own preliminary pilot studies, with elementary school children, suggesting that a human observer can  assess the affective emotional state of a student with reasonable reliability based on observation of facial expressions, gross body language, and the content and tone of speech.  If the human observer is also acting in the role of coach or mentor, these assessments can be confirmed or refined by direct conversation (e.g. simply asking the

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