Association for Public Policy Analysis & Management

The rigid tracking system used in guiding public high school students to specific course sequences has largely been dismantled over recent decades, allowing all students to participate in career and technical education(CTE). Accordingly, the descriptive characteristics of students who participate in CTE has shifted to include a greater proportion of students from high socioeconomic backgrounds as well as students who take high level math courses. The students that enroll in high level math courses may be drawn disproportionately to the CTE clusters such as engineering or computer science, although this has not been investigated in the literature. If students are sorting themselves inside of CTE on their interests or perceived ability, then the majority of CTE research that averages all CTE participants together is potentially misleading.

Evidence of this occurring can be seen in research about CTE and college enrollment. Research conducted with data collected during the era of vocational education indicates that students who participated in CTE enrolled in college in lower rates than their peers (Cellini, 2006; DeLuca, Plank, & Estacion, 2006). In contrast, research with data that was collected after vocational education was formally changed to CTE indicates that students who participated in CTE enroll in college in equal or improved rates (Dougherty, 2016, Gottfried & Plasman, 2018).

Changing research feeds into the popular narrative that CTE is more rigorous and relevant than vocational education was, providing students with a way to prepare for career options without sacrificing college readiness. Buy in to this narrative and corresponding support for CTE was expressed by congress in 2018 when more than $1 billion in annual support for CTE was signed into law with broad bi-partisan support. This support stands in contrast with the criticism that educational stakeholders voiced about vocational education for attracting low-performing students and tracking them away from college.

However, without research into student sorting within CTE, it is currently unknown how much CTE has changed since vocational education. An increasing proportion of high-achieving students within STEM categories would create a heterogenous mix of students. However, the current research practice of assessing the outcomes of all CTE students together, regardless of CTE type, carries the unstated assumption that CTE types are homogenous.

My research uses the High School Longitudinal Study of 2009, which is the latest nationally representative dataset of high school student experiences. I use student course taking patterns, including the rigor of non-CTE core academic courses, the timing of progression through commonly required high school courses, and students’ selected non-CTE elective coursework to conduct a cluster analysis. This common machine learning technique is used for pattern detection within large datasets.

My analysis indicates that CTE participation does vary as a function of academic course-taking patterns. Students with rigorous academic course profiles participate most commonly in select CTE areas (i.e., STEM, health sciences, information technology, communications, finance), but uncommonly in all others. This suggests that recent trends in CTE research may need to be revisited and the overall progress from vocational education to CTE be reassessed.