This article concerns the synergy between science learning, understanding complexity, and computational thinking (CT), and their impact on near and far learning transfer. The potential relationship between computer-based model construction and knowledge transfer has yet to be explored. We studied middle school students who modeled systemic phenomena using the Much.Matter.in.Motion (MMM) platform. A distinct innovation of this work is the complexity-based visual epistemic structure underpinning the Much.Matter.in.Motion (MMM) platform, which guided students' modeling of complex systems. This epistemic 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. In this study, we investigated students’ conceptual understanding of science, systems understanding, and CT. We also explored whether the complexity-based structure is transferable across different domains. The study employs a quasi-experimental, pretest-intervention-posttest-control comparison-group design, with 26 seventh-grade students in an experimental group, and 24 in a comparison group. Findings reveal that students who constructed computational models significantly improved their science conceptual knowledge, systems understanding, and CT. They also showed relatively high degrees of transfer—both near and far—with a medium effect size for the far transfer of learning. For the far-transfer items, their explanations included entities’ properties and interactions at the micro level. Finally, we found that learning CT and learning how to think complexly contribute independently to learning transfer, and that conceptual understanding in science impacts transfer only through the micro-level behaviors of entities in the system. A central theoretical contribution of this work is to offer a method for promoting far transfer. This method suggests using visual epistemic scaffolds of the general thinking processes we would like to support, as shown in the complexity-based structure on the MMM interface, and incorporating these visual structures into the core problem-solving activities.

本文关注科学学习、理解复杂性和计算思维 (CT) 之间的协同作用，以及它们对近端和远端学习迁移的影响。基于计算机的模型构建和知识转移之间的潜在关系仍有待探索。我们研究了使用 Much.Matter.in.Motion (MMM) 平台对系统现象进行建模的中学生。这项工作的一个显着创新是基于复杂性的视觉认知结构，它支撑了 Much.Matter.in.Motion (MMM) 平台，该平台指导学生对复杂系统进行建模。这种认知结构表明，可以通过定义实体并为其分配（1）属性、（2）动作和（3）彼此之间以及与环境之间的交互来描述和建模复杂系统。在这项研究中，我们调查了学生对科学、系统理解和 CT 的概念理解。我们还探讨了基于复杂性的结构是否可以跨不同领域转移。该研究采用准实验、前测-干预-后测-对照的比较组设计，实验组有26名七年级学生，对照组有24名七年级学生。研究结果表明，构建计算模型的学生显着提高了他们的科学概念知识、系统理解和 CT。他们还表现出相对较高的迁移程度（无论是近迁移还是远迁移），而远迁移学习的效果大小中等。对于远转移项，它们的解释包括微观层面上实体的属性和相互作用。最后，我们发现学习 CT 和学习如何复杂地思考独立地有助于学习迁移，而科学中的概念理解仅通过系统中实体的微观行为影响迁移。这项工作的一个核心理论贡献是提供了一种促进远转移的方法。这种方法建议使用我们想要支持的一般思维过程的视觉认知支架，如 MMM 界面上基于复杂性的结构所示​​，并将这些视觉结构纳入核心问题解决活动中。