Integrated Science Practices Enhanced by Computational Thinking (InSPECT)

该项目由STEM+计算伙伴关系(STEM+C)计划资助，该计划旨在促进STEM教与学中计算集成的新方法和基于证据的理解。这个项目将通过为高中生物课程开发新的课程单元来促进这一努力，这些课程单元将计算思维融入生物。这些单元将以以探究为导向的调查为特色，要求学生使用各种计算机控制的传感器、执行器、视频捕获设备和其他技术来设计程序，以解决开放式问题。学生将使用一种名为DataFlow的简化可视化编程语言来自动执行实验程序、收集数据并将其可视化，并以无线方式连接到数据共享设备。为了有效地利用这些实时资源，学生需要将自然科学的因果思维与计算机科学的计算思维技能联系起来。该项目将开发12个课程单元，包括6个背景单元、3个高度结构化的固定实验和3个开放式项目。每套两个背景单元、一个固定实验和一个开放式项目将使学生在接受开放式项目的挑战之前，通过基础知识和实验技能的发展而进步。这个项目的成果通过确定如何利用计算机和计算思维让学生更深入地参与科学探究的过程和推理，具有推动科学课堂实践的巨大潜力。该项目将进行基于设计的研究，利用头两个项目年来开发和验证科学和计算思维实践及相关活动之间整合的教学模式。在第三年，该项目将进行试点实施，以测试该项目在不同学校的26名教师教授的生物课上创新的可行性。该项目将为教师提供教学资源、技术和专业发展，旨在使他们的学生能够进行两次科学调查，总共需要四到六周的课程。将采用混合方法研究策略来描述和评估学生在应用计算思维、进行科学实践和理解与生态系统相关的核心生物学内容知识方面的收获。数据将从课堂观察、前测和后测、活动中嵌入的评估、学生截屏报告、对教师和学生的采访以及学习软件自动收集的日志数据中收集。这些不同的数据来源将被三角化，以从多个角度验证研究结果。三个问题指导这项研究：a)在什么程度和什么条件下，学生能够利用项目的计算资源进行真正的科学调查？B)当学生通过资源、方法和设计的工具进行科学实验时，在学生进行科学实践、练习计算思维和理解生物学概念的能力方面将获得哪些学习收益？哪些因素与收益的差异有关，如果有的话？和c)教师需要什么样的背景材料和帮助才能有效地制定预期的课程？具体地说，教师背景和计算机经验对于支持学生使用技术支持的实验有多重要？不同类型的教师支持对课堂教学质量有何影响？

This project is funded by the STEM+Computing Partnership (STEM+C) program, which seeks to advance new approaches to, and evidence-based understanding of, the integration of computing in STEM teaching and learning. This project will contribute to that effort by developing new curriculum units for high school biology courses that integrate computational thinking within biology. The units will feature inquiry-oriented investigations that will require students to design procedures using a variety of computer-controlled sensors, actuators, video-capture devices, and other technologies to solve open-ended problems. Students will use a simplified visual programming language, called Dataflow, to automate experimental procedures, collect and visualize data, and wirelessly link to data sharing devices. To use these real-time resources effectively, students will need to link the causal (cause and effect) thinking of the natural sciences to the computational thinking skills of computer science. The project will develop 12 curriculum units comprising 6 background units, 3 highly structured set-piece experiments, and 3 open-ended projects. Sets of two background units, 1 set-piece experiment, and one open-ended project will allow students to progress through development of foundational knowledge and experimental skills before taking on the challenge of an open-ended project. The outcomes of this project have high potential to advance classroom practices in science by identifying ways of using computers and computational thinking to engage students more deeply in the processes and reasoning of scientific inquiry.The project will conduct design-based research, using the first two project years to develop and validate a pedagogical model of integration between science and computational thinking practices and related activities. In year three, the project will undertake a pilot implementation to test the feasibility of the project's innovations in the biology classes taught by 26 teachers in diverse schools. The project will provide teachers with teaching resources, technology, and professional development designed to enable their students to undertake two science investigations requiring a total of four to six weeks of classes. A mixed-methods research strategy will be employed both to characterize and estimate student gains in applying computational thinking, carrying out science practices, and understanding core biology content knowledge related to ecosystems. Data will be gathered from classroom observations, pretests and posttests, embedded assessments in activities, student screencast reports, interviews with teachers and students, and log data automatically collected by learning software. This variety of data sources will be triangulated to validate research findings from multiple perspectives. Three questions guide the research: A) To what extent and under what conditions are students able to use the project's computational resources to undertake authentic scientific investigations? B) When students are engaged in science experimentation made possible by the resources, approach, and designed tools, what learning gains will be observed in student abilities to perform science practices, exercise computational thinking, and understand biology concepts? What factors are associated with differences in the gains, if any? and C) What kinds of background materials and assistance do teachers require for effective enactments of the intended curriculum? Specifically, how important is teacher background and experience with computers to support student use of technology-enabled experimentation? What was the impact of various kinds of teacher supports on the quality of the classroom enactments.