How Should Examples be Learned in a Production Task? An Experimental Investigation in Mathematical Problem Posing

Kazuaki Kojima, Kazuhisa Miwa, Tatsunori Matsui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

When using mathematics in problem solving in everyday life, problem solvers must recognize and formulate problems by themselves because structured problems are not provided. Therefore, it is an important task in general education to foster learner problem posing. Although learning by solving examples is adopted in general education, it may not be sufficiently effective in fostering learner problem posing because cognitive skills differ between problem solving and problem posing. This study experimentally investigated the effects of three learning activities in problem posing: learning by solving an example, learning by reproducing an example, and learning by evaluating an example. In our experiment, undergraduates were asked to pose their own new and unique problems from a base problem initially given after learning an example by solving, reproducing, or evaluating it. The results indicated that learning by reproducing the example was the most effective in fostering the composition of new problems.

Original languageEnglish
Title of host publicationProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014
PublisherThe Cognitive Science Society
Pages2501-2506
Number of pages6
ISBN (Electronic)9780991196708
Publication statusPublished - 2014
Event36th Annual Meeting of the Cognitive Science Society, CogSci 2014 - Quebec City, Canada
Duration: 2014 Jul 232014 Jul 26

Publication series

NameProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014

Conference

Conference36th Annual Meeting of the Cognitive Science Society, CogSci 2014
Country/TerritoryCanada
CityQuebec City
Period14/7/2314/7/26

Keywords

  • learning from examples
  • mathematical learning
  • problem posing
  • Production task

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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