DIP: Extending CTSiM: An Adaptive Computational Thinking Environment for Learning Science through Modeling and Simulation in Middle School Classrooms

“网络学习和未来学习技术计划”资助的工作旨在支持展望学习技术的未来，并推进我们对人们如何在技术丰富的环境中学习的了解。开发和实施（DIP）项目建立在概念验证工作的基础上，展示了所提议的新型学习技术的可能性，PI团队建立并完善了他们所提议的创新的最小可行示例，使他们能够了解未来应该如何设计和使用此类技术，并使他们能够回答有关人们如何学习，如何促进或评估学习，以及/或如何为学习设计的问题。教育中的一个重要问题是帮助学习者理解科学现象，特别是那些太小或太大、快或慢、危险或不方便亲自体验和操作的现象。帮助学习者体验这种现象的一种方法是通过建模——建立一个现象或过程的模型，然后操纵它，看看在不同的情况下会发生什么。计算机工具可用于创建这样的模型，但是模型构建虽然对学习非常有用，但对许多学习者来说是一项复杂而困难的任务。在这个团队的网络学习探索（EXP）项目中，他们开发了一种看起来很有前途的方法来帮助中学生学习建立科学现象的计算模型。他们设计了一种表达模型的视觉语言，并展示了如何在几个现象中从较简单的模型到更复杂的模型的进展，不仅可以帮助中学生学习有针对性的科学内容，还可以学习如何设计和构建模型，以及如何解释和从模型中学习。在这个后续项目中，他们以这种方法为基础，旨在扩展技术以覆盖更多的科学，自动化教师在学生参与模型构建和解释时提供的一些帮助，通过模型构建和解释系统地研究学习者在学习中面临的挑战，并确定将在这些情况下促进成功学习的教学方法。本提案的目标是提高中学生的计算思维和科学建模能力，并以一种为未来的计算科学做好准备的方式。pi的早期网络学习EXP项目探索了利用已知的学习知识作为计算思考者来指导活动排序的潜力，以培养科学中的模型构建和解释能力。他们提出的排序让中学生建立现象模型，他们在一个科学单元的课程中逐渐使其变得更加复杂，然后在下一个单元中，再次重复这种排序，但不同的内容需要更复杂或不同的建模实践。他们为模型规范设计了一种语言，希望能够培养计算思维和模型构建能力。支持该方法的软件CTSIM是一个可视化编程平台，用于对科学建模进行建模，其中包括特定学科的结构，提供了一个可扩展的体系结构，将模型构建、仿真、测试、实验和验证无缝地编织在一起，可用于跨科学领域，并连接到运行仿真的NetLogo。在这个项目中，该团队将扩展该技术以覆盖更多的科学，根据他们看到的教师提供的帮助学生构建模型并从模型中学习的内容添加适应性脚手架，确定促进从模型构建中学习的教学方法，并系统地研究学生面临的挑战以及如何应对这些挑战。他们的研究问题集中在学生同时学习科学内容和涉及建模的计算思维技能的能力，使用关联表示的来龙去脉，构建方法所需的各种反馈，以及教师的缩放问题。

The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that shows the possibilities of the proposed new type of learning technology, and PI teams build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and that allows them to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. An important issue in education is helping learners understand scientific phenomena, especially those that are too small or large, fast or slow, dangerous or inconvenient to experience and manipulate first hand. A way of helping learners experience such phenomena is through modeling -- building a model of the phenomenon or process and then manipulating it to see what happens in different circumstances. Computer tools are available for creating such models, but model building, though very useful for learning, is a complex and difficult task for many learners. In this team's Cyberlearning Exploration (EXP) Project, they developed what looks like a promising way to help middle school students learn to build computational models of scientific phenomena. They designed a visual language for expressing models and showed how progressing from less to more sophisticated models across several phenomena could help middle schoolers not only learn targeted science content but also learn how to design and build models and how to interpret and learn from models. In this follow-on project, they build on that approach, aiming to extend the technology to cover more sciences, automate some of the help teachers provide to students as they engage in model building and interpretation, systematically study the challenges learners face in learning through model building and interpretation, and identify pedagogical approaches that will foster successful learning in these circumstances. The goal of this proposal is to improve middle schoolers' computational thinking and scientific modeling capabilities in parallel with each other and in a way that prepares young learners for the computational sciences of the future. The PIs' earlier Cyberlearning EXP project explored the potential of using what is known about learning to be a computational thinker to guide sequencing of activities for fostering model-building and interpretation capabilities in science. The sequencing they proposed has middle schoolers building models of phenomena that they gradually make more sophisticated over the course of a science unit, then in the next unit, repeating that sequencing again, but with different content requiring more sophisticated or different modeling practices. They designed a language for model specification that they hoped would foster computational thinking and model-building capabilities. CTSIM, the software that supports the approach, is a visual programming platform for modeling scientific modeling that includes discipline-specific constructs, provides a scalable architecture that seamlessly weaves together model construction, simulation, testing, experimentation, and verification, is usable across science domains, and connects to NetLogo, which runs the simulations. In this project, the team will extend the technology to cover more sciences, add adaptive scaffolding based on what they saw teachers providing to help students build and learn from models, identify pedagogical approaches for promoting learning from model building, and systematically study the challenges students face and how to address those challenges. Their research questions focus on students' abilities to simultaneously learn science content and computational thinking skills involved in modeling, the ins and outs of using linked representations, the kinds of feedback needed for scaffolding the approach, and scaling issues for teachers.