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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. 

What does it mean to conduct computer science education research in a manner that ensures that the evidence produced is high quality and benefits a wide variety of students? One can pour over various guides from institutions like What Works Clearinghouse (WWC) and the American Psychology Association (APA). However, what many standards fail to include is a holistic perspective of conducting education research, including guidelines for ensuring that the aggregated data presented represents the student population that the research will ultimately serve. In this panel, we tackle both and explore approaches that have been used in other education research fields as well as those appropriate to CS education research that can be leveraged to ensure that all students' needs, experiences, cultures, identities, and voices are captured and presented in our research. 

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). 

In recent years, eight states have adopted a graduation requirement in computer science (CS), and other states are considering similar requirements. Due to the recency of these requirements, little is known about student and teacher perceptions of course(s) that fulfill the requirement and their content. This project seeks to answer the question, What are the perceptions of students who are studying CS beyond high school and CS teachers of a high school CS requirement and its content? We used a mixed methods approach that included interview transcripts from students who took CS coursework in high school and are currently studying it in college (n = 9). We also used quantitative data from a survey of CS teachers (n = 2, 238) that asked for their perceptions of a CS graduation requirement. Most of the students felt that CS should be required in high school, and there was a wide variety of sentiment regarding what content should be included in such a course. For the high school teachers, about 85% felt that CS should be required. It is perhaps not surprising that most students who studied CS in college valued it at the high school level and thus supported a graduation requirement. What is more interesting is the diversity of content that they felt should belong in such a course. These findings serve as an important consideration for those implementing a CS graduation requirement. 

There are several changes anticipated in computer science (CS) education over the next decade, including updated student standards, rapidly changing impacts of artificial intelligence (AI), and an increasing number of school systems requiring a CS class for graduation. In order to prepare for these changes – as well as to address the equity issues that have plagued CS since its inception – we engaged in a project designed to reimagine content and pathways for high school CS education. As a collaborative project, we hosted multiple events for relevant parties (including K-12 educators and administrators, higher education faculty, industry professionals, state and district CS supervisors, and CS education researchers). These events were designed to collaboratively seek input for the creation of a series of reports recommending what a CS course that satisfies a high school graduation requirement should include, how that course should align with Advanced Placement (AP) and post-secondary CS instruction, and what pathways should exist for students after that introductory high school course. The portion of the project highlighted in this article contains an analysis of data collected from focus groups (n=21), interviews (n=10), and an in-person convening of participants from K-12, post-secondary, industry, and administrative roles (n=35). The data is centered on determining what CS content is essential for all high school students. Participants considered knowledge, skills, and dispositions across a range of CS and CS-adjacent topics and, through a variety of activities, described what new content should be taught when viewing through the lens of teaching CS to high school students in the year 2030 and what content should be prioritized. Our analysis sought to delineate and synthesize their sentiments. Six major priorities emerged from our analysis: societal impacts and ethical issues, algorithmic thinking, data and analysis, inclusive computing culture, AI, and career knowledge. The significance of our findings is that they present a broad overview of what a variety of relevant parties consider to be the most important CS content for high school students; this information is important for educators, administrators, and those who develop curriculum, standards, and/or teaching tools. 

Interim Report #2 summarizes progress to date in the second phase of the Reimagining CS Pathways: High School and Beyond project. Its focus is collectively defining pathways for continued CS learning beyond a foundational high school CS course. It includes content progressions and course implementation pathways for seven concentration areas, including artificial intelligence, cybersecurity, and data science. Primary inputs were data collected at the second in-person convening held in Atlanta, GA in January 2024, in a series of virtual focus groups, and through a literature review and additional research.