As computer science (CS) becomes a foundational part of K-12 education, universities face increasing pressure to prepare pre-service teachers to deliver highly effective CS instruction. However, most teacher preparation programs lack structured pathways to develop CS teachers, and leaders of these programs often feel unprepared to provide CS instruction. Therefore, the PrepareCS Project is a three-year initiative that brings together university teams from across the United States to develop sustainable CS teacher preparation programs. This paper reports on the first year of the project, using data from focus groups with university representatives to explore the challenges they faced while developing their CS teacher pathways. Findings reveal that teams encountered issues with program constraints, policy barriers, and communication problems. Lessons learned include the importance of establishing clear roles and communication protocols, strategically building community, applying best practices for interdisciplinary virtual teams, and developing marketing plans in tandem with program development. These findings offer timely guidance to researchers, policymakers, and educational leaders seeking to meet the growing demand for CS education and expand access

Self-efficacy is a reliable predictor of academic motivation and achievement across various disciplines and age groups, including computing education. Enhancing self-efficacy through instructional experiences and tools improves motivation and achievement, making it valuable in computing education. To make use of this relationship, educators and researchers must be able to accurately measure how experiences and tools affect self-efficacy. This study replicated and extended the validation of a new self-efficacy measurement for introductory programming students developed by Steinhorst et al. (2020). It replicated features of the original study, such as using other programming-specific self-efficacy measures. The study also introduced measures of general self-efficacy, collected data in new types of courses, collected data for a new programming language, and explored changes in self-efficacy throughout courses to assess validity. The results showed robust internal consistency and construct and convergent validity of the Steinhorst instrument for both introductory programming and data structures courses, aligning with general self-efficacy theory. The findings indicate the Steinhorst instrument's adaptability across different programming languages and contexts. A key insight is the need for researchers and educators to tailor the instrument by excluding items not yet covered in the curriculum to maintain its reliability. This research enhances the understanding of the Steinhorst instrument's robustness for assessing programming self-efficacy across diverse educational settings.

There is growing attention to the need for computer science (CS) education. Stakeholders have emphasized the importance of teaching CS to all students and exposing CS to students at the early elementary level. However, there are relatively few investigations into approaches for integrating CS education into elementary classroom instruction. In this work, we explored elementary CS teachers' CS integration perceptions and practices as the first step of a CS-integrated curriculum co-design program.