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  • 标题:Using analogy to learn about phenomena at scales outside human perception
  • 本地全文:下载
  • 作者:Ilyse Resnick ; Alexandra Davatzes ; Nora S. Newcombe
  • 期刊名称:Cognitive Research
  • 电子版ISSN:2365-7464
  • 出版年度:2017
  • 卷号:2
  • 期号:1
  • 页码:21-37
  • DOI:10.1186/s41235-017-0054-7
  • 出版社:Springer International Publishing
  • 摘要:Understanding and reasoning about phenomena at scales outside human perception (for example, geologic time) is critical across science, technology, engineering, and mathematics. Thus, devising strong methods to support acquisition of reasoning at such scales is an important goal in science, technology, engineering, and mathematics education. In two experiments, we examine the use of analogical principles in learning about geologic time. Across both experiments we find that using a spatial analogy (for example, a time line) to make multiple alignments, and keeping all unrelated components of the analogy held constant (for example, keep the time line the same length), leads to better understanding of the magnitude of geologic time. Effective approaches also include hierarchically and progressively aligning scale information (Experiment 1) and active prediction in making alignments paired with immediate feedback (Experiments 1 and 2).
  • 关键词:Analogy ; Magnitude ; Progressive alignment ; Corrective feedback ; STEM education
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