Instructional support for learning with agent-based simulations: A tale of vicarious and guided exploration learning approaches

I. Dubovi, Victor R. Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Science content knowledge is essential for many applied practices within health-care professions. With this in mind, the current study seeks to promote in-depth conceptual understanding of science pharmacology content among students from health-care programs—nursing, nutrition, and health education—by introducing learning with agent-based models. While the literature shows that learning with agent-based models promotes better conceptual understanding than more traditional approaches, to achieve these potential benefits, instructional supports are needed. This study employed an experimental pre- and post-test design comparing two forms of instructional approaches for learning with agent-based models: one group learned with agent-based models using the vicarious approach, where pairs observed and collaboratively discussed recordings of others' learning with agent-based models; the other group explored agent-based models in pairs while collaboratively discussing a set of text-based prompts, the guided exploration approach. The results revealed significantly higher pharmacology learning gains following the vicarious instructional approach compared with the guided exploration of agent-based models. Thus, findings suggest that learning from observation can be comparable and even superior to the guided exploration approach with regard to the immediate knowledge gains when collaboratively dialoguing with a peer while observing dialogue of others takes place. Future research should evaluate this instructional effect on knowledge retention and with long-term interventions.

Original languageEnglish
Article number103644
JournalComputers and Education
Volume142
DOIs
StatePublished - Dec 2019

Keywords

  • Agent-based models
  • Collaboration
  • Dialogue
  • Health professions education
  • ICAP framework
  • Vicarious learning

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