The Effects of Generative Learning Strategy Prompts and Metacognitive Feedback on Learners' Self-Regulation, Generation Process, and Achievement

Open Access
Author:
Lee, Hyeon Woo
Graduate Program:
Instructional Systems
Degree:
Doctor of Philosophy
Document Type:
Dissertation
Date of Defense:
May 14, 2008
Committee Members:
  • Barbara L Grabowski, Committee Chair
  • Francis M Dwyer Jr., Committee Member
  • Pui Wa Lei, Committee Member
  • Susan M Land, Committee Member
Keywords:
  • Metacognitive Feedback
  • Self-Regulation
  • Generative Learning
  • Structural Equation Modeling (SEM)
Abstract:
Instructional designers need to understand the internal processes of learning, identify learners’ cognitive difficulties with those processes, and create strategies to overcome the difficulties. Generative learning theory explains comprehension and understanding that results from generation of relations both among concepts and between experience or prior learning and information. Knowledge generation and learner’s cognitive and metacognitive control over their learning are the key processes of learning. </P> The current study promotes the need for a combination of generative learning strategy prompts and metacognitive feedback to improve learning. Generative learning strategy prompts with metacognitive feedback support learners comprehending complex topics through facilitating their regulation, monitoring, and refinement of their learning processes and, in turn, improving their use of generative learning strategies. </p> This study extends the understanding of generative learning theory emphasizing the mediation effects of learners’ self-regulation and use of strategies. This study, also contributes to educational research practice by demonstrating how to gather and analyze overt evidence of learners’ actual interactions with the instructional interventions and by employing Structural Equation Modeling for experimental research to investigate intervening processes of learning. This approach may stimulate future research in instructional design and development for more complex, technology-enriched learning environments.