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The Promise of Learning about Learning with Adaptive Educational Technologies Roy Pea Stanford University AERA 2012, April 15 Pea, R. (2012, April 15). The promise of learning about learning with adaptive educational technologies. Invited paper for symposium: "Global Perspectives on New Technologies and Learning" of the World Educational Research Association (Eva Baker, Chair). Annual Meetings of the American Educational Research Association, Vancouver BC, Canada.

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Pea, R. (2012, April 15). The promise of learning about learning with adaptive educational technologies. Invited paper for symposium: "Global Perspectives on New Technologies and Learning" of the World Educational Research Association (Eva Baker, Chair). Annual Meetings of the American Educational Research Association, Vancouver BC, Canada.

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The Promise of Learning about Learning with Adaptive Educational

Technologies

Roy Pea

Stanford University

AERA 2012, April 15

Pea, R. (2012, April 15). The promise of learning about learning with adaptive educational technologies. Invited paper for symposium: "Global Perspectives on New Technologies and Learning" of the World Educational Research Association (Eva Baker, Chair). Annual Meetings of the American Educational Research Association, Vancouver BC, Canada.

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http://fednet.net/nae120111/

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Context• What research opportunities are possible using these

data?

• What kinds of analyses have researchers conducted using such data and what has been learned?

• What more is needed to develop research in this area?

• What are the costs and benefits of using such data for research?

• What kind of organizational supports would be needed from developers if data were used for research and program improvement?

• What other accommodations might be needed for researchers (e.g., to ensure confidentiality of data, allow data to be processed statistically, etc.)?

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Examples

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How can such technologies become trusted metacognitive resources, providing

valuable feedback and guidance to learners, teachers, and other

stakeholders?

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(1) By expanding learner profile meta-data

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(2) By expanding our data sources for

inferences about learning and its

conditions

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• Greater contextualization of

learning

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• Capturing learner-perceptible aspects of

environments

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• Capturing uses of written language and

other symbolic systems

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(3) By expanding our sense-making

techniques for gauging learning and its

conditions

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(4) By expanding access to our learner data

systems to the learner

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(5) By expanding learner access to data about

their own performances in relation to others

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(6) By providing large-scale testbeds to do experimentation in

comparative pedagogy

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• To refine theories of learning progressions

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• To theorize about more and less effective forms of

feedback and guidance during learning activities

for different learner profiles and contexts

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• To explore how to exploit social networks of learners to

build social capital for learning

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