This project will develop and study a curriculum and app that support computational thinking (CT) in a high school biology unit. The project will engage students in rich data practices by gathering, manipulating, analyzing, simulating, and visualizing data of bioelectrical signals from neural sensors, and in so doing give the students opportunities to apply CT principles.
Computational thinking (CT) is a set of processes to identify and solve problems using algorithms or steps, and can be applied not only in computer science but in other disciplines. This project will develop and study a curriculum and app that support CT in a high school biology unit. Through a month-long neural engineering unit, approximately 500 students in 18 classes will measure their own muscle and brain activity with a low-cost, portable, wearable technology. Students will then analyze the data and design a brain-computer interface to turn neural signals into real-world output (e.g., a mechanical claw controlled by brain activity). The curriculum will be supported by: (1) a web-based instructional application that will guide students through the neural engineering design process; (2) neuroscience and engineering PhD students and postdocs acting as STEM mentors; and (3) a professional development program for teachers and mentors. The goal is to increase the students’ knowledge and interest regarding neurobiology, engineering, and computational thinking. This can contribute to their long-term capacity to pursue STEM careers. By integrating CT education into high school science, this expands the accessibility of the engineering and computing experiences beyond other efforts that focus primarily on programming and computer science courses.
The project will engage students in rich data practices by gathering, manipulating, analyzing, simulating, and visualizing data of bioelectrical signals from neural sensors, and in so doing give the students opportunities to apply computational thinking principles. The project will produce curriculum materials for the neural sensors and associated data practices. It will develop an app to help students design and construct a brain-computer interface, including computational elements like coding blocks, sensor and data simulation, and connecting to external devices. The five proposed research questions of the study are: How does students’ CT change throughout their participation in the neural engineering design process? What is the cross-cultural validity of two CT scales in a sample of high school students in the US? How does the process of collecting and analyzing real-world data relate to students’ experience of he engineering design process? How do students’ attitudes toward STEM change over the course of their participation in a neural engineering design process? How does teachers’ self-efficacy for fostering CT in their students via engineering design change through their participation in professional development and in implementation of the proposed curriculum?