Abstract
Science education aims to provide students with the necessary skills and knowledge to effectively articulate and explain natural phenomena. These explanations require conceptualizing a mechanism as naturally causal employing mechanistic reasoning (MR). MR involves identifying entities involved in the underlying process of a target phenomenon, noting their actions and interactions, and explaining how these bring about the phenomenon. This study concerns the development of MR in electricity through engaging in scientific modeling practices using the Much.Matter.in.Motion(MMM) platform. It also explores how modeling practices can be fostered when supported by the epistemic complexity-based structure underpinning MMM. The epistemic complexity-based structure suggests that a complex system can be described and modeled by defining entities and assigning them (1) properties, (2) actions, and (3) interactions with each other and with their environment. This study employed a quasi-experimental, pretest-intervention-posttest-control comparison-group design with 33 eighth-grade students using MMM compared to 23 students who followed a traditional curriculum using textbooks. Both groups had six 1.5-hour sessions and answered identical pre- and posttest questionnaires. Log files while using MMM were collected. Results demonstrate that engaging in scientific modeling using MMM significantly promoted MR compared to learning by the traditional approach. Additionally, the use of MMM supported with the epistemic complexity-based structure significantly improved modeling practices in three key ways: transitioning from examining one type of entity to two types of entities at the micro level, resolving the challenge of integrating macro-level variables when unpacking the system at the micro-level and linking
Bio
Janan Saba is a lecturer at the Seymour Fox School of Education at the Hebrew University of Jerusalem, Israel, and the head of the Interactive Learning Design Lab -ILDL. Janan has a BA in Mathematics and Computer Sciences, MA in Mathematics Education, and a PhD in Learning Sciences from the University of Haifa, Israel. She was a joint postdoctoral fellow at Technion, Israel Institute of Technology, and ETH Zurich University, Switzerland. Janan’s research primarily focuses on facilitating students’ learning of science and mathematics through engagement in computer-based learning environments. In her work, she designs technology-enhanced learning environments and employs a design-based research methodology to investigate several learning approaches while exploring their associated learning outcomes and processes. In her current study, Janan is investigating how students cultivate scientific strategies when learning is based on exploring computer-based simulations and how their mechanistic reasoning is advanced. She is also focusing on the effect of learning sequence in the context of computational simulation on learning and learning transfer.