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Collaborative Technology Impacts in Distributed Learning Environments

Collaborative Technology Impacts in Distributed Learning Environments

Martha Grabowski, Greg Lepak, George Kulick
Copyright: © 2009 |Pages: 22
ISBN13: 9781605660622|ISBN10: 1605660620|ISBN13 Softcover: 9781616925918|EISBN13: 9781605660639
DOI: 10.4018/978-1-60566-062-2.ch007
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MLA

Grabowski, Martha, et al. "Collaborative Technology Impacts in Distributed Learning Environments." Innovative Mobile Learning: Techniques and Technologies, edited by Hokyoung Ryu and David Parsons, IGI Global, 2009, pp. 123-144. https://doi.org/10.4018/978-1-60566-062-2.ch007

APA

Grabowski, M., Lepak, G., & George Kulick. (2009). Collaborative Technology Impacts in Distributed Learning Environments. In H. Ryu & D. Parsons (Eds.), Innovative Mobile Learning: Techniques and Technologies (pp. 123-144). IGI Global. https://doi.org/10.4018/978-1-60566-062-2.ch007

Chicago

Grabowski, Martha, Greg Lepak, and George Kulick. "Collaborative Technology Impacts in Distributed Learning Environments." In Innovative Mobile Learning: Techniques and Technologies, edited by Hokyoung Ryu and David Parsons, 123-144. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-062-2.ch007

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Abstract

Many studies have examined the impact of collaborative technology in distributed learning environments. Few of those studies involved new collaborative technologies such as mobile computing, and few were empirically tested. This chapter addresses the need to empirically examine the impacts of new collaborative technologies including mobile, wearable, embedded, and ubiquitous technologies, on distributed learners. The chapter introduces a technology-independent framework for considering collaborative technologies, including mobile technology; it relates expected technology impacts to user preferences using a generalizable research framework rooted in the social science, communication and technology acceptance literature. The framework is updated to include the lens of contextualization richness, and the results of an empirical test of the framework are presented. The results show user preferences for technologies with a high range of design features to support cognitive learning, while showing preference for technologies with a low range of design features to support perceived learning. Next steps and implications for future work conclude the chapter.

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