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Increasingly professional development (PD) programs have been designed and implemented for pre-service and in-service teachers to acquire CS content knowledge and CS pedagogy and instructional strategies for K-12 students. This paper reports on our adaptation, implementation and research program for K-8 CS teachers across a Midwestern state. More specifically, its PD program for K-8 CS teachers consists of a summer institute with two graduate courses and a series of Saturday workshops during the subsequent academic year. This paper focuses on the two summer courses: one on CS knowledge content including computational thinking, variables, conditionals, loops, arrays, functions, and algorithms, and one instructional strategies, student pedagogy, computer-aided education resources, and community building. We report our SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of the two summer institutes involving the two courses to identify what went well and what needed improvement. This paper also reviews best practices for summer PD. 

The Adapt, Implement, and Research at Nebraska (AIR@NE) project, funded by the NSF CSforAll Researcher-Practitioner Partnership (RPP) program, examines the adaptation of a validated K-8 Computer Science (CS) curriculum in diverse school districts statewide. Our Research-Practitioner Partnership is primarily between the University of Nebraska-Lincoln, the Lincoln Public Schools, and other diverse school districts across Nebraska. Our primary goal is to study and document how different districts, including rural, predominantly minority, and Native American reservation, adopt the curriculum and broaden participation in CS. In addition, the project is developing instructional capacity for K-8 CS education with diverse learners. Our research also adapts and develops teacher and student CS assessments, and documents case studies using design-based research methodology to show how an adaptive curriculum broadens CS participation. Our Professional Development (PD) program for K-8 CS teachers is comprehensive. It consists of three summer courses for each cohort and a series of workshops during the academic year. Of the three summer courses, two are administered in the first year for a cohort: (1) an introduction to computer science course where teachers learn fundamental CS topics and programming in a high-level programming language (e.g., Python), and engage in problem solving and practice computational thinking, and (2) a course in pedagogy for teachers to learn how to teach K-8 CS, including lesson designs, use of instructional resources such as dot-and-dash robots, and assessments. Then, the following academic year after the summer, the PD program holds a series of workshops on five separate Saturdays to support teacher implementation of their lesson modules during the academic year, reflect and improve on their lessons, reinforce on CS concepts and pedagogy techniques, review and adopt alternative instructional resources, and share insights. These Saturday workshops also facilitate further community building and resource sharing. The third course occurs in the second year for a cohort, involving dissemination of research results from the team to the teachers, opportunities to discuss new resources and approaches on teaching CS concepts and computational thinking, and sharing of experiences and insights after teachers have completed one academic year of teaching CS. Unlike the first two courses that are required of teachers, this third course is an opt-in course that combines more in- depth pedagogy and elements of leadership. Thus far, we have had two cohorts and used the design methodology to revise our PD program, making our design more robust based on the lessons learned over the two years. The course materials, assessment, and survey instruments have also been improved. While the project is on-going we have data to that indicates the impact of the work so far. There were significant pre-post gains for both cohorts in teachers’ knowledge of computer science concepts and computational thinking. Scores on the computational thinking assessment were higher than those for CS concepts, which was to be expected given their CS teaching experience. Moreover, in both cohorts, the teachers’ confidence in teaching CS improved significantly.