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Problem. With many teachers in the United States just starting to learn how to teach computer science (CS), many do not have others nearby with CS teaching experience to provide support on CS practices and concepts. To address this gap, we piloted a one-year remote coaching program designed to provide that missing individualized support to teachers. Research Question. Our research question for this project was: How does teachers’ ability to apply CS practices and knowledge of CS concepts change after the coaching process? Methodology. Our mixed-methods study leveraged three primary forms of data from teachers who were coached (coachees) and teachers providing coaching (coaches): pre- and post-surveys, coaching logs, and self-reflection checklists. Findings. CS coachees’ reported CS knowledge and skills and their ability to apply CS practices related to Standard 1 were significantly higher after the coaching intervention. Implications. As more teachers continue to learn how to teach CS and hone their skills and practices, engaging these teachers in coaching can be powerful in improving their student’s learning. Professional development providers and regional education agencies (districts and schools) could leverage the processes formed through this intervention (which is based on professional development practices with solid evidence for positive impacts) to provide similar coaching to teachers just learning how to teach CS. 

Although there is extensive research on what makes teacher computer science (CS) professional development (PD) effective, little attention has been given to how PD providers prefer to collect and report data. A platform that met the needs of teachers while also simultaneously meeting the needs of funding bodies could be powerful in answering questions about participation and experiences in CS PD. Our research question for this study was: Which features and types of data do teachers find most important to include in a platform designed to record data related to their engagement with PD?We used an exploratory-sequential mixed methods approach that included focus groups and a survey created from an analysis of the data from the focus groups. The three most desired feature included adding information about the subject/topics targeted by the CS PD offerings for the CS PD they took, the grade levels targeted by the CS PD offering, and the number of training hours, points, and/or CEU credits earned or available to be earned by each CS PD offering taken. The three least desired features included have the capability for teachers to take notes about a CS PD, QR codes for signing up for CS PD, and capability to enter data about non-CS PDs you have completed. While we will use this data to inform the development of the platform, this study is significant as states can leverage this knowledge as they create their own systems for creating platforms for teachers within their own states. 

Problem. Extant measures of students’ cybersecurity self-efficacy lack sufficient evidence of validity based on internal structure. Such evidence of validity is needed to enhance confidence in conclusions drawn from use of self-efficacy measures in the cybersecurity domain. Research Question. To address this identified problem, we sought to answer our research question: What is the underlying factor structure of a new self-efficacy for Information Security measure? Method. We leveraged exploratory factor analysis (EFA) to deter- mine the number of factors underlying a new measure of student self-efficacy to conduct information security. This measure was created to align with the five elements of the information security section of the K-12 Cybersecurity Education framework. Participants were 190 undergraduate students recruited from computer science courses across the U.S. Findings. Results from the EFA indicated that a four-factor solution best fit the data while maximizing interpretability of the factors. The internal reliability of the measure was quite strong (𝛼 = .99). Implications. The psychometric quality of this measure was demonstrated, and thus evidence of validity based on internal structure has been established. Future work will conduct a confirmatory factor analysis (CFA) and assess measurement invariance across sub- groups of interest (e.g., over- vs. under-represented race/ethnicity groups, gender).