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Learning computer science (CS) is increasingly becoming a necessary component of K–12 education, but in most cases, teachers do not have either the essential knowledge to teach or a curriculum to follow. In this article, we analyze the outcomes from a yearlong, blended professional development (PD) program to teach teachers game design and coding skills, and we codevelop a middle school curriculum. Using a mixed-methods design, this study presents the findings as we investigate how teachers experienced the PD program. Our findings show that the teachers’ level of engagement differed for various reasons, such as challenges related to time, motivation, and interest. We identified a high-engagement group who developed a deep understanding of coding knowledge and engaged in the PD activities more. We also identified a low-engagement group who failed to attend most activities despite mentioning generic interests. We provide an explanation why these teachers might have varied in terms of their engagement and offer how “data” can be used to predict engagement levels and as a diagnostic tool (e.g., lacked specificity in explaining CS concepts in reflections). We recommend add- ing motivational strategies and better tracking and monitoring mechanisms for better engagement and PD design considerations for online and blended learning for teachers. 

Interest influences adults’ and young learners’ learning in formal and informal contexts. Although interest and interest development frameworks have been used in research on student-learning, they are not used in teacher-focused research, especially as “outcomes” of teacher professional development (PD) activities. In this study, we used interest development as the outcome of PD in computer science (CS) and investigated the factors that influenced teachers’ (n = 5) interest development toward CS using various data sources and analysis methods. We found that interest development is (a) varied, (b) influenced by self-relation, knowledge, and affect, (c) associated with reengagement with PD activities, and (d) it can be captured using computational text analysis methods and online log data.