Representational afford ances for collaborative learning in technology-enhanced environments

Bodong Chen, Feng Lin

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Human learning is increasingly multi-representational. Despite substantial efforts to design multiple external representations for individual learning, little has been systematically synthesized about how external representations can be mobilized to help multiple learners learn together. In this chapter we first outline five key challenges facing collaborative learning including: (a) Establishing and maintaining a joint problem space, (b) Communicating with one another, (c) Creating a shared knowledge base, (d) Supporting epistemic practices; and (e) Coordinating, monitoring, and regulating collaborative processes. We then illustrate the ways in which multiple representations are provided to mitigate these challenges and to harness multiple perspectives of learners to surpass their individual understanding.

Original languageEnglish (US)
Title of host publicationHandbook of Learning from Multiple Representations and Perspectives
PublisherTaylor and Francis
Pages513-531
Number of pages19
ISBN (Electronic)9780429813665
ISBN (Print)9780367001179
DOIs
StatePublished - Jan 1 2020

Bibliographical note

Publisher Copyright:
© 2020 Peggy Van Meter, Alexandra List, Doug Lombardi and Panayiota Kendeou. All rights reserved.

Keywords

  • Collaborative learning
  • Computer tools
  • Computer-mediated communication
  • External representations

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