The effect of different sequences of examples and problems on learning experimental design

Eman Ganaiem, Ido Roll

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

Abstract

Example-based learning refers to a pedagogy in which learners are provided with a step-by-step solution to novel problems early in the learning process. Worked examples are most effective when used in tandem with open problems. However, there are still questions regarding the ordering of examples and problems. We compared the effect of sequencing examples and problems on learning and cognitive load. One hundred thirteen middle-school students were randomly assigned to one of three conditions that varied presentation order within each example-problem pair: example first, problem first, or simultaneous presentation. We evaluated students' performances on knowledge application and the transfer of learning on the topic of experimental design. We found no effect neither on learning nor on cognitive load. We compare these results to parallel studies in the literature and identify possible reasons for the null effect.

Original languageEnglish
Title of host publicationInternational Collaboration toward Educational Innovation for All
Subtitle of host publicationOverarching Research, Development, and Practices - 16th International Conference of the Learning Sciences, ICLS 2022
EditorsClark Chinn, Edna Tan, Carol Chan, Yael Kali
Pages727-734
Number of pages8
ISBN (Electronic)9781737330653
StatePublished - 2022
Event16th International Conference of the Learning Sciences, ICLS 2022 - Virtual, Online, Japan
Duration: 6 Jun 202210 Jun 2022

Publication series

NameProceedings of International Conference of the Learning Sciences, ICLS

Conference

Conference16th International Conference of the Learning Sciences, ICLS 2022
Country/TerritoryJapan
CityVirtual, Online
Period6/06/2210/06/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 4 - Quality Education
    SDG 4 Quality Education

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Education

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