Investigating Interaction Dynamics: A Temporal Approach to Team Learning

Lida Z. David, Maaike D. Endedijk*, Piet Van den Bossche

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

5 Citations (Scopus)
36 Downloads (Pure)

Abstract

Teams are at the core of every organisation, composed of individuals who continuously collaborate, exchange knowledge and ideas, and constantly learn from one another through formal or informal learning experiences. Team learning is therefore a continuously changing phenomenon that develops and evolves over time as teams interact. In this chapter, we aim to promote the investigation of team learning as a temporal phenomenon, and suggest that its temporality can be captured through team interaction dynamics, defined as continuously changing patterns of micro-behaviours that emerge and evolve as teams operate. We set three key steps for initiating and leading research that captures temporality: (a) identifying the interaction dynamics of interest, (b) figuring out the best way to collect and code these, and finally (c) choosing an analysis technique that helps capture continuously and sequentially unfolding patterns. We offer some ‘food for thought’ on interaction dynamics that relate to team learning and the added value of investigating them, and present some existing data collection and coding methods. We finally propose a framework for choosing an appropriate analysis technique based on the dynamic output that each analysis generates.

Original languageEnglish
Title of host publicationMethods for Researching Professional Learning and Development
PublisherSpringer
Pages187-209
Number of pages23
ISBN (Electronic)978-3-031-08518-5
ISBN (Print)978-3-031-08517-8
DOIs
Publication statusPublished - 2022

Publication series

NameProfessional and Practice-based Learning
Volume33
ISSN (Print)2210-5549
ISSN (Electronic)2210-5557

Keywords

  • Analysis techniques
  • Emergent states
  • Interaction dynamics
  • Patterns
  • Team learning
  • Temporal phenomenon
  • 2024 OA procedure

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