Integration of Environmental Chemistry and Computing to Advance Evidence-based Reasoning, Problem Solving, and Computational Thinking in Middle School Students

计算已经成为现代科学、技术、工程和数学（STEM）领域日常实践的一个组成部分。因此，STEM+Computing Partnerships（STEM+C）计划旨在推进STEM教学和学习中计算整合的新方法和基于证据的理解。计算思维（CT）是一种智力技能，其根源在于构思出可以由计算机有效操纵的有意义的、基于信息的表示的能力。将CT整合到核心科学教学中解决了K-12教育的实际限制，因为课程中没有空间直接教授给每个人。但是，更重要的是，将CT融入核心科学提供了一个协同的机会，以深化两者的教学。有效促进CT技能发展的STEM教育计划是培养未来STEM劳动力的最成功的教育计划之一，这些劳动力可以利用计算方法来推进基础科学和工程研究，并设计创新方法，利用丰富的数据资源来解决复杂的社会相关问题。 同样，一个科学素养的社会越来越需要理解科学知识的进步如何与大数据、建模和模拟的使用交织在一起，如果他们要做出明智的、基于证据的决策的话。该项目将探索核心化学主题和计算思维的整合，通过学生与模型的互动和操作，直观地表示化学系统，如碳循环。 当学生检查一个可视化的、可运行的模型，做出预测，修改编程代码，然后再次运行模型时，计算机会向学生提供反馈，要求解释。 预测、解释和测试计算机反馈的过程，再加上结构化的论证过程，支持学生在探索和学习计算和物理世界时像科学家一样思考和行动的能力。弗吉尼亚理工大学和德克萨斯大学奥斯汀分校将研究一种通过在所有学生都参加的背景下提供CT思维来扩大CT思维的方法：8年级综合科学有针对性的化学主题将嵌入到可编辑，可运行的计算模型中，这些模型本身嵌入到结构化科学论证的教学法中，重点是基于证据的推理。学生将学习化学系统，以及平衡和反应速率等关键概念，通过与模型进行交互，因为它们是可视化表示的，并通过修改和调整代码来更好地表示化学系统的各个方面。 四个计算化学任务，包括计算机模型和相关的标准一致的课程，将开发和完善使用参与式，基于设计的研究技术，与四个奥斯汀，得克萨斯州的教师合作，为来自各种社会经济环境的约300名学生服务。 一个课程框架，称为参数驱动的计算思维和两个相关的评估工具-计算思维的性能评估和计算态度测试-将开发和验证。 录像课堂教学活动，学生文物，和计算机使用日志的定量和定性分析将被用来记录这种新模式的有效性和广泛的实用性，整合化学和计算。 将通过该项目培训一名博士后研究员和两名研究生。

Computing has become an integral part of everyday practice within modern fields of science, technology, engineering, and math (STEM). As a result, the STEM+Computing Partnerships (STEM+C) program seeks to advance new approaches to, and evidence-based understanding of, the integration of computing in STEM teaching and learning. Computational Thinking (CT) is an intellectual skill rooted in the ability to conceive of meaningful, information-based representations that can be effectively manipulated by a computer. Integrating CT into core science instruction addresses practical constraints in K-12 education, in that there is no room in the curriculum to teach it directly to everyone. But, more importantly, integrating CT into core science provides a synergistic opportunity to deepen instruction in both. STEM education programs that effectively foster development of CT skills as an essential competency are among the most successful in preparing a future STEM workforce that can leverage computational methods to advance basic science and engineering research and design innovative approaches for using data-rich resources to solve complex, societally-relevant problems. Similarly, a science-literate society increasingly needs to appreciate how advances in scientific knowledge are inter-woven with the use of big data, modeling, and simulations, if they are to make informed, evidence-based decisions. This project will explore the integration of core chemistry topics and computational thinking through student interaction with and manipulation of models that visually represent chemical systems, such as the carbon cycle. When a student examines a visual, runnable model, makes a prediction, changes the programming code, and then runs the model again, the computer provides feedback to the student that demands explanation. The process of anticipating, interpreting, and testing the computer's feedback, when coupled with a structured argumentation process, supports a student's abilities to think and act like a scientist as they explore and learn about both the computational and physical world.Virginia Tech and the University of Texas at Austin will investigate a method of broadening access to CT thinking by providing it in a context that all students take: 8th Grade Integrated Science. Targeted chemistry topics will be embedded into editable, runnable computational models that are themselves embedded within a pedagogy of structured scientific argumentation focused on the production of evidence-based reasoning. Students will learn about chemical systems, as well as key concepts such as equilibrium and reaction rates, through interacting with models as they are represented visually and about CT through modifying and adapting the code to better represent aspects of the chemical systems. Four computational chemistry tasks, including computer models and associated standards-aligned curricula, will be developed and refined using participatory, design-based research techniques, in partnership with four Austin, TX teachers serving ~300 students from a variety of socio-economic settings. A curricular framework called Argument Driven Computational Thinking and two associated assessment tools - the Performance Assessment of Computational Thinking and the Computing Attitudes Test - will be developed and validated. Quantitative and qualitative analysis of videotaped classroom instructional activities, student artifacts, and computer-use logs will be used to document the effectiveness and broad utility of this new model for integrating chemistry and computing. A post-doctoral fellow and two graduate students will be trained through this project.