Context and Deep Learning Design

Autor/inn/en	Boyle, Tom; Ravenscroft, Andrew
Titel	Context and Deep Learning Design
Quelle	In: Computers & Education, 59 (2012) 4, S.1224-1233 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0360-1315
DOI	10.1016/j.compedu.2012.06.007
Schlagwörter	Instructional Design; Design Requirements; Learning Processes; Learning Theories; Educational Technology; Educational Strategies; Context Effect + Suchen Sie Ihr Suchwort? Lesson concept; Lessonplan; Unterrichtsentwurf; Design preferences; Gestaltungsmittel; Learning process; Lernprozess; Learning theory; Lerntheorie; Unterrichtsmedien; Lehrstrategie
Abstract	Conceptual clarification is essential if we are to establish a stable and deep discipline of technology enhanced learning. The technology is alluring; this can distract from deep design in a surface rush to exploit the affordances of the new technology. We need a basis for design, and a conceptual unit of organization, that are applicable across constant technological change. These are the issues addressed in this article. The article first explores the nature of "deep learning design" where the aim is to shape the possibilities of the technology to most effectively enhance learning. These design insights need to be applied to a unit of organization that is not dependent on any particular technology. They should interact with and shape technology possibilities rather than be narrowly defined by them. The key unit of organization proposed is that of context. At a theoretical level, the article explores context as a shared interpretation of situation. The implications of the nested nature of contextual interpretation on design, implementation and evaluation are explored in depth. The internal dynamics of learning contexts are then discussed initially in terms of principles, heuristics and scripts. The contribution of this article is to present a coherent argument for context as the central unit for deep learning design, and to articulate the incisive theoretical and practical consequences of this position. (Contains 3 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10