Fostering Student Computational Thinking with Self-Regulated Learning

Purpose of the Grant

This award-winning project (VA Projects That Work, 2023) advances research and development of new transdisciplinary approaches to computational STEM teaching and learning that integrates the fields of Computational Thinking (CT) and Self-Regulated Learning (SRL) into science activities in four content areas: Earth Science, Biology, Chemistry, and Physics. The project provides professional development (PD) for high school teachers that includes instruction on CT, SRL, and on using SPIN (Science Practices Innovation Notebook). Teachers have collaboratively developed lessons that infuse CT & SRL, upload the lessons into SPIN, implement those lessons in their classrooms, and then collaboratively analyze student work samples captured by SPIN. You can get a SPIN account at spin.cehd.gmu.edu.

Throughout this PD process, researchers collected multiple forms of data by observation, surveys, SPIN documents and artifacts as part an overall research plan. The research team employed data analytics to uncover patterns and develop a CT learning progression for grades 9-12. The project implemented four phases of iterative Design Research. In the Informed Exploration phase, researchers and practitioners worked together to find opportunities to teach CT through SRL processes during STEM investigations. The Enactment Phase involved short iterative tests of design conjectures to develop and build the electronic notebook. The Local Impact phase engaged practitioners in testing the electronic notebook in their classes while researchers document teaching implementation and student learning of CT. In the Broader Impact phase, the electronic notebook was scaled up to local high school teachers who did not participate in the project implementation. See the research page on this site for results from this research (http://csesquared.gmu.edu/research/).