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

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.