CER: Assessing relationships between high school teacher professional development and student outcomes in computer science and artificial intelligence

Many high school students do not have the chance to take computer science classes. This project aims to help more students gain access to computer science by training teachers in new ways to teach important topics like problem-solving, computer basics, and how computers can be used responsibly. The project is expected to increase student interest in computer science, stay in computer science classes, and even choose careers in technology. It will work with one of California's largest school districts (Elk Grove Unified School District) and has the potential to improve education and job opportunities for thousands of students. The results could also provide insights to other schools across the country to better support student learning about computers and technology. This project builds on previous work to expand access to computer science in high schools by using a researcher-practitioner partnership to study the effects of teacher professional development on student motivation and retention in computational courses. The research will use design-based methods to investigate how training teachers in design thinking, core computer science concepts, and key artificial intelligence topics affects student motivation, engagement, and enrollment in information and communication technology pathways. The project will involve collaboration between university faculty and public school educators in order to increase the number of participating schools, teachers, and students. By generating new evidence on how teacher training impacts student outcomes, this work will contribute to the field of computer science education and inform future practices aimed at student recruitment and retention in computational subjects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.