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为支持设想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的努力提供资金。开发和实施（DIP）项目建立在概念验证工作的基础上，展示了拟议的新型学习技术的可能性，PI团队建立并完善了他们提出的创新的最低可行性示例，使他们能够理解未来应该如何设计和使用这种技术，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，and/or how to design设计for learning学习.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、太快或太慢、太危险或太不方便而无法亲身体验和操作的现象。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此类模型，但模型构建尽管对于学习非常有用，但对于许多学习者来说却是一项复杂且困难的任务。 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。他们设计了一种用于表达模型的视觉语言，并展示了如何在几种现象中从不太复杂的模型发展到更复杂的模型，这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，以及如何解释和学习模型。在这个后续项目中，他们建立在这种方法的基础上，旨在将技术扩展到涵盖更多的科学，自动化教师为学生提供的一些帮助，因为他们参与模型构建和解释，系统地研究学习者在学习中面临的挑战通过模型构建和解释，并确定教学方法，这将促进在这些情况下成功的学习。该提案的目标是同时提高中学生的计算思维和科学建模能力，并为未来的计算科学做好准备。PI早期的Cyberlearning EXP项目探索了使用学习成为计算思想者的知识来指导活动顺序的潜力，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，再次重复该排序，但不同的内容需要更复杂或不同的建模实践。他们设计了一种模型规范语言，希望能培养计算思维和模型构建能力。CTSIM是支持该方法的软件，是一个可视化编程平台，用于建模科学建模，包括特定学科的结构，提供了一个可扩展的架构，无缝编织在一起的模型构建，模拟，测试，实验和验证，可用于跨科学领域，并连接到NetLogo，运行模拟。在这个项目中，该团队将扩展该技术，以涵盖更多的科学，根据他们看到的教师提供的内容添加自适应脚手架，以帮助学生建立和学习模型，确定促进学习的教学方法，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建模中涉及的科学内容和计算思维技能的能力，使用链接表示的来龙去脉，构建方法所需的反馈类型，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.