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Instructional Heuristics for the Use of Worked Examples to Manage Instructional Designers’ Cognitive Load while Problem-Solving

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Abstract

Research focusing on the complexities faced by instructional designers have called for pedagogical strategies to equip instructional designers with the ability to problem solve and make decisions. One of the most widely studied strategies for managing cognitive load is the use of worked examples, which provides an alternative to traditional problem solving by presenting the learner with a step-by-step model for approaching and solving a problem. This paper synthesizes the literature examining how worked examples are being used as an instructional strategy to promote solving ill-structured problems in various disciplines. It is argued that incorporating instructional heuristics utilizing worked examples will alleviate cognitive load imposed on instructional designers as they acquire skills to demonstrate mastery in conceptual, strategic, and conditional knowledge domains.

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Authors and Affiliations

  1. Old Dominion University, 4128 Education, Norfolk, VA, 23529, USA

    Justin Sentz & Jill Stefaniak

  2. University of Georgia, 221 Rivers Crossing, 850 College Station Road, Athens, GA, 30602, USA

    Jill Stefaniak

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  1. Justin Sentz
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  2. Jill Stefaniak
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Correspondence to Jill Stefaniak.

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This research was conducted while Jill Stefaniak was a faculty member at Old Dominion University in Norfolk, VA. This author is now affiliated with the University of Georgia in Athens, GA.

Appendices

Appendix 1

Table 2 Analysis of studies on learner expertise and cognitive load (part 1)
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Table 3 Analysis of studies on learner expertise and cognitive load (part 2)
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Appendix 2

Table 4 Analysis of studies on worked examples for ill-structured problems (part 1)
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Table 5 Analysis of studies on worked examples for ill-structured problems (part 2)
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Sentz, J., Stefaniak, J. Instructional Heuristics for the Use of Worked Examples to Manage Instructional Designers’ Cognitive Load while Problem-Solving. TechTrends 63, 209–225 (2019). https://doi.org/10.1007/s11528-018-0348-8

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