Worked examples are more efficient for learning than high-assistance instructional software

Bruce M. McLaren, Tamara van Gog, Craig Ganoe, David Yaron, Michael Karabinos

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

The ‘assistance dilemma’, an important issue in the Learning Sciences, is concerned with how much guidance or assistance should be provided to help students learn. A recent study comparing three high-assistance approaches (worked examples, tutored problems, and erroneous examples) and one low-assistance (conventional problems) approach, in a multi-session classroom experiment, showed equal learning outcomes, with worked examples being much more efficient. To rule out that the surprising lack of differences in learning outcomes was due to too much feedback across the conditions, the present follow-up experiment was conducted, in which feedback was curtailed. Yet the results in the new experiment were the same: there were no differences in learning outcomes, but worked examples were much more efficient. These two experiments suggest that there are efficiency benefits of worked example study. Yet, questions remain. For instance, why didn’t high instructional assistance benefit learning outcomes and would these results hold up in other domains?
Original languageEnglish
Title of host publicationArtificial Intelligence in Education
PublisherSpringer
Pages710-713
Number of pages4
ISBN (Electronic)978-3-319-19773-9
ISBN (Print)978-3-319-19772-2
DOIs
Publication statusPublished - 17 Jun 2015
EventArtificial Intelligence in Education (AIED) - Madrid, Spain
Duration: 22 Jun 201526 Jun 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9112

Conference

ConferenceArtificial Intelligence in Education (AIED)
Country/TerritorySpain
CityMadrid
Period22/06/1526/06/15

Keywords

  • Assistance dilemma
  • Classroom studies
  • Empirical studies
  • Worked examples
  • Erroneous examples
  • Tutored problems to solve
  • Problem solving

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