Varying effects of subgoal labeled expository text in programming, chemistry, and statistics

  • Lauren E. Margulieux
  • Richard Catrambone
  • Laura M. Schaeffer
Article

Abstract

Originally intended as a replication study, this study discusses differences in problem solving performance among different domains caused by the same instructional intervention. The learning sciences acknowledges similarities in the learners’ cognitive architecture that allow interventions to apply across domains, but it also argues that each domain has characteristics that might affect how interventions impact learning. The present study uses an instructional design technique that had previously improved learners’ problem solving performance in programming: subgoal labeled expository text and subgoal labeled worked examples. It intended to replicate this effect for solving problems in statistics and chemistry. However, each of the experiments in the three domains had a different pattern of results for problem solving performance. While the subgoal labeled worked example consistently improved performance, the subgoal labeled expository text, which interacted with subgoal labeled worked examples in programming, had an additive effect with subgoal labeled worked examples in chemistry and no effect in statistics. Differences in patterns of results are believed to be due to complexity of the content to be learned, especially in terms of mapping problem solving procedures to solving problems, and the familiarity of tools used to solve problems in the domain. Subgoal labeled expository text was effective only when students learned more complex content and used unfamiliar problem solving tools.

Keywords

Worked examples Expository text Discipline based education research STEM education Instructional design Subgoal learning 

Notes

Acknowledgements

This research was supported by the American Psychological Foundation’s and Council of Graduate Departments of Psychology’s Graduate Research Scholarship. The authors would like to thank John Sweller and Mark Guzdial for their feedback. We also thank Gerin Williams for her help collecting data.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Learning Technologies DivisionGeorgia State UniversityAtlantaUSA
  2. 2.Georgia Institute of TechnologyAtlantaUSA

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