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.
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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.
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