UC Irvine

Computing skills such as programming, graphic design, and networking are among the fastest growing in the modern workplace. Computing skills are increasingly critical in many roles including STEM (Malyn-Smith & Lee, 2012) and non-STEM occupations (Grinis, 2019). The American education system responded to this need by creating more educational opportunities for youth to prepare for these roles. For example, the number of California students attending schools offering a CS course has grown from 33.2% to 79.1% since 2010 (Bruno & Lewis, 2021). The growth is designed to support workforce readiness in computing-related occupations.

Federal policy frames Career and Technical Education (CTE) coursework as part of a college and career readiness strategy preparing youth for high-paying, in-demand occupations. Computing is one of many areas students can choose to concentrate in (defined as taking two or more credits in the field) during high school. Despite being the most popular secondary CTE concentration, computing coursework is being offered and taken in patterns unrelated to their utility value (Sublett & Griffith, 2019). Generally, there is little evidence to suggest why certain students choose or avoid certain CTE pathways (Leu & Arbeit, 2020). Where there is evidence, students draw from a multitude of factors including, but not limited to, future career interests (DeFeo, 2015; Fletcher & Cox, 2012). Given the broad labor market applicability of computing skills relative to other CTE concentrations, there is little evidence to guide policy-makers and researchers to design and investigate inclusive computing education pathways. We respond to this need by addressing the following research question:

- What are the school- and student-level factors associated with taking two or more computing courses during high school?

The present study investigates the factors associated with becoming a computing concentrator in high school. The prior literature suggests that computing coursework is framed as a workforce-readiness strategy though that does not play out in the data and little is known about what motivates students to pursue these courses. We apply Situated Expectancy Value Theory

(SEVT; Eccles & Wigfield, 2020) to model who takes two or more computing courses during high school using a two-level logistic regression model on data from the nationally representative High School Longitudinal Study of 2009 (HSLS:09).