The Effectiveness of Pedagogical Agents’ Prompting and Feedback in Facilitating Co-adapted Learning with MetaTutor

  • Roger Azevedo
  • Ronald S. Landis
  • Reza Feyzi-Behnagh
  • Melissa Duffy
  • Gregory Trevors
  • Jason M. Harley
  • François Bouchet
  • Jonathan Burlison
  • Michelle Taub
  • Nicole Pacampara
  • Mohamed Yeasin
  • A. K. M. Mahbubur Rahman
  • M. Iftekhar Tanveer
  • Gahangir Hossain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7315)

Abstract

Co-adapted learning involves complex, dynamically unfolding interactions between human and artificial pedagogical agents (PAs) during learning with intelligent systems. In general, these interactions lead to effective learning when (1) learners correctly monitor and regulate their cognitive and metacognitive processes in response to internal (e.g., accurate metacognitive judgments followed by the selection of effective learning strategies) and external (e.g., response to agents’ prompting and feedback) conditions, and (2) pedagogical agents can adequately and correctly detect, track, model, and foster learners’ self-regulatory processes. In this study, we tested the effectiveness of PAs’ prompting and feedback on learners’ self-regulated learning about the human circulatory system with MetaTutor, an adaptive, multi-agent learning environment. Sixty-nine (N=69) undergraduates learned about the topic with MetaTutor, during a 2-hour session under one of three conditions: prompt and feedback (PF), prompt-only (PO), and no prompt (NP) condition. The PF condition received timely prompts from several pedagogical agents to deploy various SRL processes and received immediate directive feedback concerning the deployment of the processes. The PO condition received the same timely prompts, without feedback. Finally, the NP condition learned without assistance from the agents. Results indicate that those in the PF condition had significantly higher learning efficiency scores than those in both the PO and control conditions. In addition, log-file data provided evidence of the effectiveness of the PA’s timely scaffolding and feedback in facilitating learners’ (in the PF condition) metacognitive monitoring and regulation during learning.

Keywords

self-regulated learning metacognition pedagogical agents co-adaptation multi-agent systems learning product data process data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Roger Azevedo
    • 1
  • Ronald S. Landis
    • 2
  • Reza Feyzi-Behnagh
    • 1
  • Melissa Duffy
    • 1
  • Gregory Trevors
    • 1
  • Jason M. Harley
    • 1
  • François Bouchet
    • 1
  • Jonathan Burlison
    • 3
  • Michelle Taub
    • 1
  • Nicole Pacampara
    • 1
  • Mohamed Yeasin
    • 4
  • A. K. M. Mahbubur Rahman
    • 4
  • M. Iftekhar Tanveer
    • 4
  • Gahangir Hossain
    • 4
  1. 1.Dept. of Educational and Counselling PsychologyMcGill UniversityMontrealCanada
  2. 2.Illinois Institute of TechnologyCollege of PsychologyChicagoUSA
  3. 3.Dept. of PsychologyUniversity of MemphisMemphisUSA
  4. 4.Dept. of Electrical and Computer EngineeringUniversity of MemphisMemphisUSA

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