Effects of model-centered instruction on effectiveness, efficiency, and engagement with ill-structured problem solving

Jeongmin Lee, J. Michael Spector

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The goals of this study were to investigate the timing and the mechanism by which two types of model-centered instruction (MCI)-expert modeling (EM) and selfguided modeling (SGM)-might be made increasingly effective, efficient, and engaging for learners with different levels of expertise. The 62 pre-service and in-service evaluators who participated in the study were randomly assigned to one of these two types of MCI. The participants in the EM group were provided with the conceptual models used by experts to solve ethical conflicts within program evaluation. The participants in SGM group received no guidance in developing their own mental models. Regarding instructional effectiveness measures, there were no significant differences between the two types of MCI. However, inexperienced learners in the EM group invested less instructional effort and time than did those in the SGM group. In addition, inexperienced learners in the EM group also exhibited more engagement than did those in the SGM group. Therefore, EM is likely to be the more appropriate instructional design for inexperienced learners. Expert modeling required experienced learners to invest more mental effort, because if the conceptual model of the expert was redundant, it required them to integrate the previous schema with resulting overload of their working memory. Regardless of the types of MCI employed, the inexperienced participants showed significantly higher levels of attention and satisfaction than did the experienced participants.

Original languageEnglish
Pages (from-to)537-557
Number of pages21
JournalInstructional Science
Volume40
Issue number3
DOIs
StatePublished - May 2012

Keywords

  • Expert modeling
  • Expertise reversal effect
  • Ill-structured problem solving
  • Model-centered instruction
  • Self-guided modeling

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