Are you looking to learn more about developing and executing research plans to best meet the needs of your CS education projects? Utilizing case studies from the evaluation research group at the Texas Advanced Computing Center intermixed with hands-on activities, this workshop will help you along your research or program evaluation journey! As a workshop participant, you will work through guided practice activities in small groups to deepen your understanding of how to design research and evaluation methods for your program. Key takeaways from this tutorial will be the steps and considerations for developing a research or evaluation plan that aligns data collection activities and analysis to your research or evaluation questions. Instrumentation will also be shared throughout, with participants taking away examples of surveys and interview protocols that have been effectively used by the team at the Texas Advanced Computing Center. Resources will also be provided on how to find additional instruments and how to assess and repurpose those for your projects. This tutorial will help you determine which data is most critical to collect and which methods are best suited for collecting that data based on your participants and program needs and resources.

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.

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.

Quantitative research in CS education has suffered from inattention to complexities inherent in measuring educational equity. This study aims to tease apart the complexities of educational equity and advance the field by developing a disparity index for quantifying inequities and using it to investigate the importance of accounting for intersectionality and distinguishing between access to and participation in CS education. This descriptive study analyzed student demographic and course-taking data for N=1,537,073 high school students in Texas. Results showed the disparity index can be a useful tool for quantifying and assessing equity in CS education. Disparities in terms of access to and participation in CS education were compounded for students who were members of multiple underrepresented subpopulations (e.g., rural Black females). Disparities differed between measures of access and participation. Implications of this study are that accounting for the intersectionality of students' multiple social identities and distinguishing between access and participation in quantitative measures are key to understanding (and thus addressing) the complexities of educational equity.

A complex web of factors can influence whether students participate in computer science (CS) during high school. In order to increase participation in CS for all students, we need to better understand who is currently participating and what factors might be hindering participation. This study utilized a large-scale, student-level dataset from the Texas Education Research Center to investigate factors that predict high school student participation in CS and advanced CS courses. Our dataset contained information on over 1.1 million Texas high school students from the 2017-2018 school year, allowing us visibility into CS course availability in schools, student course taking, and detailed demographic information. We used multilevel mixed-effects logistic regression models to explore predictive factors of student participation in CS and advanced CS courses, limiting our analysis to students whose schools offered CS. In both models, our results showed that students who took Algebra I before high school had more than double the odds of being enrolled in a CS course. This work supports and extends previous understanding of factors that are predictive of CS participation in high school, contributing to the existing literature by uncovering the importance of Algebra I before high school as a potential gatekeeper to participation in CS.