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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. 

In the United States, state learning standards guide curriculum, assessment, teacher certification, and other key drivers of the student learning experience. Investigating standards allows us to answer a lot of big questions about the field of K-12 computer science (CS) education. Our team has created a dataset of state-level K-12 CS standards for all US states that currently have such standards (n = 42). This dataset was created by CS subject matter experts, who - for each of the approximately 10,000 state CS standards - manually tagged its assigned grade level/band, category/topic, and, if applicable, which CSTA standard it is identical or similar to. We also determined the standards' cognitive complexity using Bloom's Revised Taxonomy. Using the dataset, we were able to analyze each state's CS standards using a variety of metrics and approaches. To our knowledge, this is the first comprehensive, publicly available dataset of state CS standards that includes the factors mentioned previously. We believe that this dataset will be useful to other CS education researchers, including those who want to better understand the state and national landscape of K-12 CS education in the US, the characteristics of CS learning standards, the coverage of particular CS topics (e.g., cybersecurity, AI), and many other topics. In this lightning talk, we will introduce the dataset's features as well as some tools that we have developed (e.g., to determine a standard's Bloom's level) that may be useful to others who use the dataset. 

The future of AI will be determined in part by how its developers are educated. Thus, how computer science (CS) education incorporates instruction in various aspects of AI will have a substantial impact on AI's evolution. Understanding how and what CS educators think about AI education is, therefore, an important piece of the landscape in anticipating -- and shaping -- the future of AI. However, little is known about how educators perceive the role of AI education in CS education, and there is no consensus yet regarding what AI topics should be taught to all students. This paper helps to fill that gap by presenting a qualitative analysis of data collected from high school CS instructors, higher education CS faculty, and those working in the tech industry as they reflected on their priorities for high school CS instruction and on anticipated changes in high school, college, and workplace CS. We conclude with recommendations for the CS education research community around AI in K-12, particularly at the high school level. 

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. 

Traditionally, computer science (CS) in the United States has been an elective subject at the high school level. In recent years, however, some school systems have created a CS graduation requirement. Designing a required CS course that meets the needs of anticipated future advancements in the field necessitates exploring the research question, To better understand what these different groups perceive to be the essential content of a foundational high school CS course, we conducted a series of focus groups. These focus groups explored participants' (n = 21) thinking about what content would be most important to prioritize in a required high school CS course. Transcripts of the focus groups were abductively coded and then analyzed to determine what CS content priorities were identified and what disagreements about priorities exist. We found that participants (1) emphasized CS knowledge and skills, with minimal reference to dispositions, (2) prioritized content similar to that found in current CS standards, (3) developed broad, high-level descriptions of content, (4) identified contextually relevant factors, (5) foregrounded AI both a tool and as a subdomain of CS, and (6) emphasized computational thinking. These findings can inform further research on the design and implementation of a required high school CS course designed to meet the needs of the future as well as to support revisions of CS standards for high school students. 

Interim Report #1 summarizes progress to date in the first phase of the Reimagining CS Pathways: High School and Beyond project. Its focus is collectively defining what CS content is essential for all high school students. Primary inputs were data collected at the first in-person convening held in Chicago, IL in November 2023, in a series of virtual focus groups, and through a literature review and additional research. 

Teacher professional development (PD) is a key factor in enabling teachers to develop mindsets and skills that positively impact students. It is also a key step in building capacity for computer science (CS) education in K-12 schools. Successful CS PD meets primary learning goals and enable teachers to grow their self-efficacy, asset and equity mindset, and interest in teaching CS. As part of a larger study, we conducted a secondary analysis of CS PD evaluation instruments (). We found that instruments across providers were highly dissimilar with limited data collected for measures related to teacher learning, which has implications for future K-12 CS education. Likewise, the instruments were limited in being connected to student learning and academic growth. As a way to enable PD providers to construct measures that align with known impacting factors, we offer recommendations for collecting demographic data and measuring program satisfaction, content knowledge, pedagogical content knowledge, growth and equity mindset, and self-efficacy. We also highlight questions for PD providers to consider when constructing their evaluation, including reflecting community values, the goals of the PD, and how the data collected will be used to continually improve CS programs.