There is greater demand for Computer Science (CS) to be taught in elementary education as more states pass policies requiring it. Integrating CS into elementary education provides a viable avenue to teach CS to all students and can result in more equitable outcomes. However, our research demonstrates many elementary educators have concerns about not having enough time, training, knowledge, curricula, resources, and/or support to teach or integrate CS in their general education classrooms. This session will bring together educators, curriculum developers, professional learning providers, researchers, and practitioners interested in fostering CS and computational thinking skills among young learners. Topics will include how CS is currently being taught or integrated into elementary education, reasons why teachers are or are not integrating CS, resources and supports to address barriers to teaching CS, and what is needed to increase the integration of CS in elementary education on larger scales.

Understanding teachers' motivation to teach computer science (CS) plays a significant role in recruiting, supporting, and retaining CS teachers. Prior literature has identified the existence of differences among female and male teachers in terms of their motivation to teach. The goal of the current study was to examine the psychometric properties and measurement invariance of the Revised Motivation to Teach Computer Science (MTCS_R) scale between female and male groups. The MTCS_R scale is a shortened, more concise version of the original Motivation to Teach Computer Science (MTCS) scale, which measures teachers' motivation to teach CS on a continuum from external to internal motivation. We used the MTCS_R scale to collect survey data during 2022 and 2023 from 310 educators enrolled in a professional learning course designed to prepare teachers for a CS certification exam. We then conducted a confirmatory factor analysis with all survey respondents (N=310) and further examined measurement invariance among those who disclosed their gender (N=298). Results from the confirmatory factor analysis suggested satisfactory psychometric properties of the MTCS_R scale. In addition, we identified strong evidence to support the configural, metric, and scalar invariance across the gender groups, confirming that the MTCS_R scale is a valid measure of motivation to teach CS for both females and males. This study represents a significant advancement in the measurement of motivation to teach CS. Implications of using this instrument to assess teachers' motivation in CS teaching and further refinement of the instrument are discussed.

Background and Context: Inequities in computing education have been identified based on gender, race, ethnicity, and economic status. However, extant quantitative research tends to treat demographics as siloed categories instead of accounting for the fact that many students are members of multiple minoritized groups. There is also a conspicuous lack of research that examines equity issues at multiple stages of the education pipeline. Objectives: Our research questions asked: 1) How are gender, race/ethnicity, and economic status related to the likelihood that students will enroll in computing courses in high school? 2) How are these factors related to the likelihood of enrollment in college computing courses? 3) How do these factors relate to wage earnings for students who majored in computer science in college? Method: This study analyzed education and workforce data in the United States using multilevel logistic and linear regression analyses to identify disparities among students from multiple minoritized groups at three stages of life: high school (N=135,961), college (N=199,230), and career (N=1,251). Findings: Compounding course enrollment disparities were present at the high school level for students who are Black or Hispanic/Latino/a and female, economically disadvantaged, or both. Similar results were observed at the college level. Only gender was a statistically significant predictor of wage earnings five years after college graduation, but results of this analysis may be attenuated by the fact that relatively few students who were members of multiple minoritized groups graduated with a computing degree. Implications: These findings demonstrate the importance of considering students’ intersecting identities when assessing equity in computing education. They also lay groundwork for understanding how early inequities persist across the education pipeline and how understanding disparities at one stage can inform interpretation of disparities at subsequent stages.

Most research on equity in access to and participation in computer science (CS) courses in high school has focused on general, siloed categories of students (e.g., ethnicity, gender). Using logistic regression, this study examines multiple student-, school-, and district-level predictors for access to and participation in CS. We found that the strongest predictors of whether a school offered CS were the racial/ethnic composition of the student population and the percentage of individuals in the community who hold a bachelor's degree. In examining participation in CS, we found significant differences between different intersections of race/ethnicity and gender. These results can inform interventions and policies to increase equity by signaling which factors should be taken into consideration, for whom, and in what situations.

Motivation is a powerful driver of teachers' decisions to enter the teaching profession and why they stay in the field. As computer science education (CSEd) expands and the need for well-prepared computer science (CS) teachers grows, understanding what motivates teachers to teach CS will help address challenges related to recruiting, preparing, and retaining teachers. This poster presents the development of a scale that measures teachers' motivation to teach CS. We used exploratory and confirmatory factor analyses to test and revise the scale. This resulted in a reliable, 18-item scale that measures four distinct, but related, constructs of teacher motivation to teach CS: external pressures, external benefits, student benefits, and personal enjoyment. Researchers and practitioners can utilize this scale to understand what motivates teachers to become CS educators and explore how such motivations can be leveraged to strengthen CSEd.