Search for: All records

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). 

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. 

Within K-12 computing education, the building blocks that contribute to student success and equitable outcomes are broadly captured in the CAPE framework (i.e., capacity, access, participation, experience). However, these broad com- ponents provide limited detail on the important factors that can support academic achievement, particularly within each component. Our research question for this study was: What are factors comprising each component of CAPE that support academic achievement among K-12 CS students?To answer this question, we first created an a priori set of factors based on previous research findings that have been found to contribute to academic achievement. After organizing these factors within each CAPE component, we conducted a systematic mapping review of K-12 CS education research (2019-2021) (n = 196) from publicly available peer-reviewed articles from the K-12 CS Education Research Resource Center. Through this mapping, we identified an additional set of factors that have been studied by CS education researchers and added these to our set of factors. More importantly, we found that capacity was the component investigated the most frequently and access was the least. There are many areas (or categories) within each component that remain unstudied (i.e., dual credit offerings, career guidance), even though they play a role in computing education. The expanded CAPE framework is now publicly available and can be used to inform researchers and practitioners about what each CAPE component comprises. These factors are accompanied by descriptions of each factor. Not only does it highlight the many factors to be considered when designing and delivering computing education to K-12 students, it also provides a solid framework for future research that synthesizes or analyzes homogeneous factors or explores how various factors may be correlated. 

Problem. To investigate and identify promising practices in eq- uitable K-12 and tertiary computer science (CS) education, the capacity for education researchers to conduct this research must be rapidly built globally. Simultaneously, concerns have arisen over the last few years about the quality of research that is being con- ducted and the lack of research that supports teaching al students computing. Research Question. Our research question for our study was: In what ways can existing research standards and practices inform methodologically sound, equity-enabling computing education research? Methodology. We conducted a concept analysis using existing re- search and various standards (e.g. European Educational Research Association, Australian Education Research Organisation, Ameri- can Psychological Association). We then synthesised key features ni the context of equity-focused K-12 computing education research. Findings. We present aset of guidelines for general research design that takes into account best practices across the standards that are infused with equity-enabling research practices. Implications. Our guidelines wil directly impact future equitable computing education research by providing guidance on conducting high-quality research such that the findings can be aggregated and impact future policy with evidence-based results. Because we have crafted these guidelines to be broadly applicable across a variety of settings, we believe that they will be useful to researchers operating in a variety of contexts.