Much.Matter.in.Motion: Learning Science through Constructing Computational Models of Complex Systems

Janan Saba, Sharona T Levy

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The paper presents a study into students' learning of science through constructing computational models of complex systems with the new Much.Matter.in.Motion platform (MMM; Levy, Saba, et al., 2018). MMM’s design highlights the epistemological structure of agent-based modelling: defining entities, their properties, actions and interactions with each other and with macro-level boundaries and fields. The study was conducted with six 7th-grade students and focused on conceptual learning of the topic of gases with the MMM environment; reasoning about complex systems; and on the use of the platform's affordances, using questionnaires, interviews, and screen-captures. Results show a significant rise in the overall scores; though mainly for the micro-level science concepts. Interviews’ analysis showed greater changes in texture of the students’ explanations: Shifting from static to dynamic descriptions, compounding different steps, used more science concepts more correctly, and relating the micro-level rules to the macro-level patterns. Three of the modelling platform's affordances were used in activity's screen-capture movies: Macro-level objects are painted in, to remove the need of programming these objects. The use of numerical measurements at initial stages is minimized in service of supporting observation of the complex phenomena itself. Ease in changing the model, to encourage the shifts between successive models.
Translated title of the contributionחומר.רב.בתנועה: למידת מדעים על-ידי בניית מודלים חישוביים של מערכות מורכבות
Original languageEnglish
Title of host publicationהאדם הלומד בעידן הטכנולוגי: כנס צ'ייס למחקרי טכנולוגיות למידה (קובץ)
Publisherהאוניברסיטה הפתוחה ושה"ם
Pages14 (2019), 39E-44E
Number of pages31
StatePublished - 2019
Externally publishedYes

IHP publications

  • IHP publications
  • Science -- Study and teaching
  • School children
  • Learning
  • Education -- Data processing

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