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New Forms of Deep Learning on the Web: Meeting the Challenge of Cognitive Load in Conditions of Unfettered Exploration in Online Multimedia Environments

New Forms of Deep Learning on the Web: Meeting the Challenge of Cognitive Load in Conditions of Unfettered Exploration in Online Multimedia Environments

Michael DeSchryver, Rand J. Spiro
ISBN13: 9781605669823|ISBN10: 1605669822|EISBN13: 9781605669830
DOI: 10.4018/978-1-60566-982-3.ch133
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MLA

DeSchryver, Michael, and Rand J. Spiro. "New Forms of Deep Learning on the Web: Meeting the Challenge of Cognitive Load in Conditions of Unfettered Exploration in Online Multimedia Environments." Web Technologies: Concepts, Methodologies, Tools, and Applications, edited by Arthur Tatnall, IGI Global, 2010, pp. 2563-2581. https://doi.org/10.4018/978-1-60566-982-3.ch133

APA

DeSchryver, M. & Spiro, R. J. (2010). New Forms of Deep Learning on the Web: Meeting the Challenge of Cognitive Load in Conditions of Unfettered Exploration in Online Multimedia Environments. In A. Tatnall (Ed.), Web Technologies: Concepts, Methodologies, Tools, and Applications (pp. 2563-2581). IGI Global. https://doi.org/10.4018/978-1-60566-982-3.ch133

Chicago

DeSchryver, Michael, and Rand J. Spiro. "New Forms of Deep Learning on the Web: Meeting the Challenge of Cognitive Load in Conditions of Unfettered Exploration in Online Multimedia Environments." In Web Technologies: Concepts, Methodologies, Tools, and Applications, edited by Arthur Tatnall, 2563-2581. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-982-3.ch133

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Abstract

We claim that the Web has the potential to be a quintessential multimedia environment for complex learning, particularly in ill-structured domains. This chapter explores the cognitive load considerations associated with several aspects of deep and extended learning on the Web. We also propose the need for a reconceptualization of Cognitive Load Theory for comprehension and learning in more ill-structured conceptual arenas. This reconceptualization emphasizes the need for learning approaches that promote flexible knowledge assembly through processes of organic, reciprocal, and deep Web learning.

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