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Erschienen in: Technology, Knowledge and Learning 1/2018

09.01.2017 | Original Paper

PBL Group Autonomy in a High School Environmental Science Class

verfasst von: D. Mark Weiss, Brian R. Belland

Erschienen in: Technology, Knowledge and Learning | Ausgabe 1/2018

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Abstract

With increasing class sizes, teachers and facilitators alike hope for learning groups where students work together in self-contained and autonomous ways requiring reduced teacher support. Yet many instructors find the idea of developing independent learning in small groups to be elusive particularly in K-12 settings (Ertmer and Simons in Interdiscip J Probl Based Learn 1(1):5, doi:10.​7771/​1541-5015.​1005, 2006; Simons and Klein in Inst Sci 35(1):41–72, 2007). Autonomy in small learning groups is actively promoted in many of the forms of problem-based learning (PBL). A PBL learning group is traditionally defined as students with tutor assigned to the group. However, as PBL is introduced into K-12 environments, the assumption of group autonomous functioning is tenuous for a variety of reasons. The purpose of this research is to determine the extent to which a K-12 PBL learning group functions in autonomous ways when a computer-based scaffold provides some student support normally provided by the teacher or tutor during a PBL environmental science unit. If a computer-based scaffold can successfully enable key elements of autonomous group functioning by supplying process level support normally provided by a tutor or teacher, K-12 teachers anxious to attempt PBL may be able to venture with greater confidence that they can provide multiple learning groups adequate support. Results suggest that a computer-based scaffold informed by Moore’s transactional distance theory can promote process elements of autonomous group functioning.

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Metadaten
Titel
PBL Group Autonomy in a High School Environmental Science Class
verfasst von
D. Mark Weiss
Brian R. Belland
Publikationsdatum
09.01.2017
Verlag
Springer Netherlands
Erschienen in
Technology, Knowledge and Learning / Ausgabe 1/2018
Print ISSN: 2211-1662
Elektronische ISSN: 2211-1670
DOI
https://doi.org/10.1007/s10758-016-9297-5

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