Promoting Learning Transfer by Constructing Computational Models of Complex Systems in Science among Middle School Students

Janan Saba, Hagit Hel-Or, Sharona T Levy

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The paper concerns synergy between science education, complex systems and, computational thinking (CT) through constructing computational models using Much.Matter.in.Motion (MMM) platform. It focuses on transfer ability of complexity-based structure, which underlies MMM, across different domains. The complexity-based structure suggests that a system can be described and modeled by defining entities, their actions, and interactions. We compared learning of seventhgrade students using MMM with students’ learning following a normative curriculum using textbooks. Results show: the experimental group successfully promoted their conceptual learning, systems understanding, and CT; they showed relatively high degrees of near and far transfer, with a medium effect size for far transfer; Independent contributions of learning CT and learning systems on learning transfer; conceptual understanding indirectly impacts transfer.
Translated title of the contributionקידום העברה של למידה על ידי בניית מודלים חישוביים של מערכות מורכבות במדע בקרב תלמידי חטיבת הביניים
Original languageEnglish
Title of host publicationהאדם הלומד בעידן הדיגיטלי: ספר הכנס השסעה-עשר לחקר חדשנות טכנולוגית וטכנולוגיות למידה ע"ש צ'ייס
Subtitle of host publicationהאוניברסיטה הפתוחה. המרכז לחקר חדשנות בטכנולוגיות למידה
Editorsיורם עשת, אינה בלאו, אבנר כספי, ניצה גרי, יורם קלמן, תרצה לוטרמן, יעל סידי
Place of Publicationרעננה
Publisherהאוניברסיטה הפתוחה, מרכז המחקר לשילוב טכנולוגיות בהוראה ע"ש צ'ייס
Pages17 (2022), E17-E27
StatePublished - 2022
Externally publishedYes

IHP publications

  • IHP publications
  • Science -- Study and teaching
  • Junior high school students
  • Learning
  • Education -- Data processing
  • Complexity (Philosophy)

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