Then and Now: Depicting a Changing National Profile of STEM Career and Technical Education Course Takers


by Jay Stratte Plasman, Michael A. Gottfried & Ethan L. Hutt - 2020

Background: After nearly a century of federal policies focusing on career-related high school coursework, a 2006 policy reauthorization especially called for increased rigor in STEM-themed career and technical education (CTE) courses and increased participation from all students, and particularly women and those with disabilities. We explore whether this reauthorization helped meet these calls for increased participation.

Research Questions: We asked the following research questions in exploring the implementation of the Perkins IV act: (1) How have the predictors of participation in AS-CTE coursework changed during the decade between 2004 and 2013? (2) Were students in the class of 2013 more likely to participate in AS-CTE than those in the class of 2004? (3) Is there a specific difference in AS-CTE participation for female students and students with disabilities in the class of 2013 as compared to the class of 2004?

Research Design: To respond to these questions, we merged two nationally representative datasets—the Education Longitudinal Study of 2002 (ELS:2002) and the High School Longitudinal Study of 2009 (HSLS:2009). We employed basic logistic regression to explore changes in participation and ordinary least squares regression to explore changes in credit accumulation. We also utilized double hurdle and state fixed-effects models to account for various potential biases.

Results: We found that there were slight changes in predictors of applied STEM CTE course-taking, though female students remained significantly less likely than male students to participate in each cohort. Exploring across cohorts, we found students in the later cohort (HSLS:2009) to be both more likely to participate in applied STEM CTE and more likely to complete more units. Finally, when exploring female students and students with IEPs, we found that these students were more likely to participate in applied STEM CTE, but were not more likely to complete more units.

Conclusions: A first implication from these findings is that it appears the national efforts and sentiments around increasing high schoolers’ participation in CTE course-taking have taken hold. Second, it appears there may be a specific role for states as they look to grow participation in applied STEM CTE—and CTE in general. Finally, additional focus needs to be placed on increasing CTE participation for underrepresented students.



Career and technical education (CTE) high school coursework has been a focus of federal education policy dialogue and funding for the past 100 years. The federal government first became directly involved in CTE—called vocational education at the time—with the passage of the Smith-Hughes Act of 1917. The charge of this Act was to provide federal funding for schools to train teachers and to operate and instruct vocational programs. The overarching goal was to improve the national economy by providing rising student cohorts with technical skills that would directly serve them in the labor market . In 1963, the Vocational Education Act firmly entrenched vocational education in secondary schools and sought to use vocational education as a means to address both the labor needs of the Cold War as well as social inequalities. In 1984, the Vocational Education Act was reauthorized as the Carl D. Perkins Vocational Education Act. This Perkins legislation was reauthorized once more in 1998, and again in 2006 as the Carl D. Perkins Career and Technical Education Improvement Act (Perkins IV). The most recent reauthorization has emphasized a move away from vocational education as strictly a skill-building platform and toward CTE as an academic pursuit that has since become aligned with the movement for improved college and career readiness .


With each reauthorization since 1917, CTE policy has placed less emphasis on coursework that solely provides technical skills and more emphasis on developing college and career preparedness . This change in CTE policymaking has been in line with a general shift in social policy toward providing foundations for furthering educational opportunities rather than a strict focus on job skills , while also aligning with educational policy sentiments aimed at increased college access . In this regard, the current 2006 form of CTE, with such a sharpened focus on preparing students for both postsecondary and employment opportunities, is meant to appeal to a broader range of students than ever before—both those who choose to enter the workforce immediately after high school as well as those who choose to pursue college options .


In contrast to its predecessors, the current Perkins IV sought to emphasize academic rigor in part by encouraging the expansion of CTE coursework especially in science, technology, engineering, and mathematics (what Gottfried, Bozick, and Srinivasan [2014] first titled as “Applied STEM CTE,” or “AS-CTE” for short). These AS-CTE courses, which include engineering and information technology, encourage the alignment of applicable job-related skills with academically challenging coursework targeted to students at all ability levels (Gottfried & Bozick, 2016; Gottfried et al., 2014). As examples of these courses, Appendix A presents the most common AS-CTE courses taken in our analyzed cohorts of data in this study.


AS-CTE has particularly been emphasized in Perkins IV given the identification of a STEM labor market shortage by many , and thus broadening STEM course-taking participation through AS-CTE could have significant individual- and societal-level educational and employment pipeline benefits. To this effect, Perkins IV specifically emphasized the importance of encouraging broad CTE participation for groups of students who have been traditionally underrepresented in STEM courses—namely female students and students with disabilities . Beyond participation gaps in STEM high school coursework, female students and students with disabilities also continue to be underrepresented in college STEM studies and later STEM careers (Gottfried & Sublett, 2017; Plasman & Gottfried, 2016). This is problematic, then, given that recent research has established a relationship between AS-CTE course-taking in high school and later STEM pursuits in postsecondary education (Plasman, Gottfried, & Sublett, 2017). Therefore, encouraging entry into STEM via AS-CTE high school coursework may have been one of the motivating factors in the Perkins reauthorization in order to “prepare students, including special populations, for subsequent employment in high skill, high wage occupations (including those in which mathematics and science skills are critical), or for participation in postsecondary education” (Perkins, p. 22).


Over the years, career-related education policy has been dynamic, shifting with respect to both the presumed beneficiary (i.e., earlier reauthorizations focused on non-college-bound students, whereas the current reauthorization focuses more generally across a broader range of students) as well as perceived changes in the labor market (i.e., STEM jobs). In addition, perhaps more than at any time before, STEM courses within the CTE framework are becoming more appealing to a broad range of students as a viable and valuable pathway. Given these shifts and evolving CTE policies, it is surprising that little work has looked at if and how participation in AS-CTE coursework in high school has changed over the past decade. Research into shifts in patterns and prevalence are especially relevant given that an explicit goal of the 2006 reauthorization was to broaden participation, particularly in STEM areas and particularly for underrepresented groups. Ours is the first known study to assess the last decade of CTE-related changes with respect to course-takers, and thus can serve as a guidepost for future policy making.


THE ROLE OF AS-CTE


Over the past few decades, the stagnant or dwindling number of students in STEM areas has been cited as a specific area of national need by the U.S. federal government . Considering that AS-CTE courses are intended to link high school STEM content to both college and career, taking high school AS-CTE courses may have the potential to help address these national concerns about a “leaky” STEM pipeline (Gottfried & Bozick, 2016; Gottfried & Plasman, 2018; Sublett, 2016). For example, AS-CTE participation is connected to higher odds of participating in advanced math and science academic courses (such as Advanced Placement) as well as improved math achievement in the 12th grade (Gottfried, 2015; Gottfried et al., 2014). Participation in high school AS-CTE coursework is also linked to persistence in STEM fields in college and increased chances of employment immediately after high school (Dougherty, 2016; Plasman et al., 2017). In terms of career, one known study found that students concentrating in AS-CTE clusters in high school earned significantly more than students who did not concentrate in AS-CTE .


Therefore, given the stated national need to increase persistence in STEM fields in school and in the workforce and given the established benefits of AS-CTE courses, it is important to understand if and how the profile of AS-CTE course-takers has changed before and after the most recent Perkins reauthorization that encouraged a broadening of participation of students in these areas. Equipped with this knowledge, we can see who is poised to reap the established benefits of these courses. Furthermore, the fact that over the past two decades federal policy has explicitly emphasized the broadening participation of different student groups in CTE courses, and particularly in STEM areas, provides us with a unique opportunity to address the AS-CTE course-taking profile over the most recent and important period of change in the history of CTE policymaking.


In sum, considering the changes in policy focus that took place before and after the 2006 Perkins IV reauthorization with regards to CTE—i.e., increased labor market demand alignment of CTE courses, move away from “vocational” and toward “career and technical” education, focus on STEM, and broadening of participation—and a potential Congressional reauthorization of the Act, we look to examine the AS-CTE course-taking patterns as they existed before and after the most recent reauthorization. We respond to this void by asking the following questions:


(1)

How have the predictors of participation in AS-CTE coursework changed during the decade between 2004 and 2013?

(2)

Were students in the class of 2013 more likely to participate in AS-CTE than those in the class of 2004?

(3)

Is there a specific difference in AS-CTE participation for female students and students with disabilities in the class of 2013 as compared to the class of 2004?


The first question allows us to identify what factors predicted AS-CTE participation prior to and after the Perkins IV reauthorization. To examine the high school class of 2004, we used the Education Longitudinal Study of 2002 (ELS). This dataset followed a cohort of students who were 10th graders in 2002 through their high school experiences and beyond. To examine the high school class of 2013, we used the High School Longitudinal Study of 2009 (HSLS). This dataset followed a cohort of ninth-grade students in 2009 as they progressed through high school and into the various postsecondary pursuits. In addressing the second question, we can directly observe if and how the population of AS-CTE course-takers changed over time, thereby addressing whether the federal government might be meeting its goals of broadening participation in AS-CTE courses.


The final question is of particular interest. Female students and students with disabilities represent two populations who continue to be underrepresented in STEM fields in college as well as STEM careers . Therefore, participation in AS-CTE in high school may provide the opportunity to alleviate this college and career readiness gap in STEM. Previous research has shown the potential for engineering CTE coursework in high school to help encourage female students to major in engineering in college at a higher rate than male students, thereby potentially helping to close the engineering gap (Gottfried & Plasman, in press). Plasman and Gottfried (2016) found that students with disabilities were more likely to graduate from high school after taking AS-CTE courses. Considering the positive benefits of AS-CTE participation for these groups, by addressing this question we will be able to provide evidence as to whether more students in key subgroups are indeed poised to benefit from these courses being offered in high school.


METHOD


DATASET OVERVIEW


In this study, we examined two nationally representative datasets. First, ELS followed a cohort of students who were enrolled in their sophomore year in high school in 2002. These students were followed as they progressed through high school and into college and eventually into their careers. Spring 2002 was the base year for the study, at which point questionnaires were collected from students, parents, teachers, and administrators. Students were then re-surveyed in spring 2004, and official high school transcript data were added to the dataset in 2005.


Second, HSLS followed a cohort of students who were enrolled in their freshman year in high school in 2009. They were also tracked through high school, with 2009 serving as the base year for the study and follow-ups taking place in the spring of 2012 and the fall of 2013. High school transcripts for HSLS were collected in 2014. Data from 2014 represent the most recent available data.


The transcript data across both datasets includes rich information on student course-taking history, grades earned, and credits attempted and earned. Based on course codes included in the transcript files, individual courses could be identified as either academic or CTE. Additionally, these course codes allowed us to identify CTE courses that fell specifically into the AS-CTE category. Across both datasets, credits for course completion have been standardized to Carnegie units—one Carnegie unit is defined as a single course taken for a one-hour period every day throughout the school year—which allows us to make equivalent comparisons between the two datasets. Transcript data was checked to ensure students only received credit for passed classes, there were no duplicate records, and credits were compatible with each school’s calendar.


Individuals were included in the final analytic sample if they had complete transcript data. To account for missing data on other variables, we employed a multiple imputation technique to impute 20 datasets as recommended by Graham et al. (2007). After imputation, the final analytic sample included 14,920 observations from ELS, and 21,930 observations from HSLS, for a total sample of 36,850. All sample sizes were rounded to the nearest 10, per NCES guidelines. To ensure representativeness of the sample, we included NCES survey weights.


OUTCOME: AS-CTE COURSE-TAKING


AS-CTE courses definitively fall into two of the CTE categories as coded by the U.S. Department of Education in both datasets: engineering technologies and computer and information sciences (Gottfried et al., 2014). We broke out the course-taking outcome in two specific ways. First, we identified whether a student participated in any AS-CTE coursework in high school. This was a binary indicator identifying whether a student enrolled in at least one AS-CTE course during his or her high school career.


A second AS-CTE course-taking outcome was the number of units completed in AS-CTE. We chose to look at units as opposed to number of courses completed because different individual classes may be worth different numbers of units. For example, a yearlong course would count as one full unit but only a single course, whereas a student taking two semester-long courses would also receive one full unit but through completion of two courses. Examining units allows for a more standardized analysis (Plasman et al., 2017). Across the ELS sample, 50.7% participated in AS-CTE in high school, and completed an average of 0.54 AS-CTE units. From the HSLS sample, 57.3% participated in AS-CTE courses and completed an average of 0.68 AS-CTE units. Descriptively, we see that students in the United States are taking more AS-CTE units in the 2009 high school cohort compared to the cohort from 2002.


CONTROL VARIABLES


Both ELS and HSLS contain identical socio-demographic and academic variables as well as school variables. Descriptive statistics for these variables are presented in Table 1. The means are weighted to ensure national representativeness. In addition to the means on selected variables for the full cohorts, we also present the means for students who participated in AS-CTE. This shows how participation may be shifting across both students and schools. The selected variables were identified from previous research on AS-CTE course-taking, CTE course-taking in general, and high school course-taking in academic math and science courses . These variables were sourced primarily from the base-year surveys, except for math self-efficacy, which was taken from the first follow-up surveys, and academic history variables, which were taken from the transcript files. Across both datasets, all included variables are binary unless otherwise noted. Hence, we have identical measures in both datasets, with variables being sourced from identical data collection waves.


            

Table 1. Mean Values for All Included Covariates by Cohort

 

 

 

 

 

 

 

ELS (n = 14,800)

 

HSLS (n = 21,900)

 

Total

 

AS-CTE takers

 

Total

 

AS-CTE takers

 

Mean

Std. Dev.

 

Mean

Std. Dev.

 

Mean

Std. Dev.

 

Mean

Std. Dev.

AS participation

0.49

(0.50)

 

    —

 —

 

0.56

(0.50)

 

    —

 —

AS units

0.54

(0.85)

 

1.10

(0.93)

 

0.68

(0.97)

 

1.21

(1.01)

Student Level

           

Gender (Female)

0.50

(0.50)

 

0.42

(0.49)

 

0.50

(0.50)

 

0.45

(0.50)

Race/Ethnicity

           

Black

0.14

(0.35)

 

0.14

(0.35)

 

0.14

(0.35)

 

0.14

(0.35)

Asian

0.04

(0.20)

 

0.04

(0.21)

 

0.04

(0.19)

 

0.03

(0.17)

Hispanic

0.16

(0.36)

 

0.15

(0.36)

 

0.23

(0.42)

 

0.22

(0.41)

White

0.61

(0.49)

 

0.62

(0.49)

 

0.51

(0.50)

 

0.53

(0.50)

Other

0.01

(0.09)

 

0.01

(0.10)

 

0.09

(0.28)

 

0.08

(0.28)

SES (quintile)

2.89

(1.38)

 

2.87

(1.37)

 

2.79

(1.39)

 

2.78

(1.38)

Family Arrangement

           

Single parent

0.23

(0.42)

 

0.23

(0.42)

 

0.31

(0.46)

 

0.32

(0.46)

Both biological parents

0.58

(0.49)

 

0.59

(0.49)

 

0.53

(0.50)

 

0.54

(0.50)

Other arrangement

0.17

(0.38)

 

0.17

(0.37)

 

0.14

(0.34)

 

0.14

(0.34)

Academic History and Attitudes

          

Ninth-grade GPA

2.54

(0.92)

 

2.54

(0.90)

 

2.58

(0.93)

 

2.60

(0.90)

Math score (% correct)

52.07

(16.39)

 

52.62

(16.02)

 

53.99

(16.48)

 

54.22

(15.97)

Academic units

17.57

(4.99)

 

17.71

(4.60)

 

17.39

(6.11)

 

17.80

(5.38)

Non-AS-CTE units

2.54

(2.29)

 

2.51

(2.27)

 

2.18

(2.22)

 

2.23

(2.22)

Math self-efficacy (decile)

5.38

(2.87)

 

5.45

(2.84)

 

5.31

(3.00)

 

5.39

(3.00)

IEP

0.14

(0.35)

 

0.12

(0.32)

 

0.23

(0.41)

 

0.23

(0.42)

Education Expectations

           

Two-year degree

0.16

(0.36)

 

0.17

(0.37)

 

0.07

(0.26)

 

0.07

(0.26)

Four-year degree or more

0.69

(0.46)

 

0.69

(0.46)

 

0.75

(0.43)

 

0.72

(0.45)

School Level

           

Percent ELL

4.74

(8.65)

 

4.50

(8.72)

 

6.29

(10.41)

 

5.86

(10.49)

Percent minorities

34.42

(30.67)

 

33.27

(30.93)

 

40.28

(31.26)

 

38.17

(30.67)

Percent FRL

25.64

(23.83)

 

25.67

(24.02)

 

39.13

(25.23)

 

39.08

(24.68)

Urbanicity

           

Urban

0.30

(0.46)

 

0.30

(0.46)

 

0.32

(0.47)

 

0.32

(0.46)

Suburban

0.50

(0.50)

 

0.49

(0.50)

 

0.45

(0.50)

 

0.44

(0.50)

Rural

0.20

(0.40)

 

0.22

(0.41)

 

0.23

(0.42)

 

0.24

(0.43)

Geographic Location

           

West

0.23

(0.42)

 

0.23

(0.42)

 

0.23

(0.42)

 

0.20

(0.40)

South

0.34

(0.48)

 

0.30

(0.46)

 

0.38

(0.48)

 

0.40

(0.49)

Northeast

0.18

(0.39)

 

0.20

(0.40)

 

0.17

(0.38)

 

0.18

(0.39)

Midwest

0.24

(0.43)

 

0.27

(0.44)

 

0.22

(0.41)

 

0.22

(0.42)

Note. Variables in this table are binary except for GPA and parent involvement (0–4 scales); academic units, CTE units, and applied STEM units; math self-efficacy (1–10 scale); SES (1–5), math score (0–100), and school demographic variables (continuous 0–100).


Sociodemographic data included gender, race/ethnicity, family arrangement, and a variable identifying socioeconomic status quintile. Academic variables included ninth-grade GPA, a standardized math score, postsecondary expectations, whether or not the student had an Individualized Education Plan (IEP) on file, math self-efficacy, number of academic units completed, and number of non-AS CTE units completed. This final measure of other CTE completed is an especially important variable to include considering potential CTE programmatic changes that may have taken place during the interim timeframe. School variables were sourced from the base-year school survey in both the ELS and HSLS datasets. The school-level variables included percent of school population in ELL, percent of school population eligible for free or reduced-price lunch, and percent of school population that is minority. We also control for urbanicity and geographic location.


ANALYTIC APPROACH


Research Question 1


To address our first research question about AS-CTE participation and unit completion in each of the ELS and HSLS subsamples separately, we used a logistic regression model. Prediction of AS-CTE participation was estimated as follows:


[39_23035.htm_g/00002.jpg]


In this model, [39_23035.htm_g/00004.jpg] is a binary outcome representing whether student i in school s participated in AS-CTE courses. [39_23035.htm_g/00006.jpg] is a vector representing each of the student- and school-level variables identified in the variables section above. Finally, [39_23035.htm_g/00008.jpg] is the error term estimated with standard errors adjusted for high school clustering. The coefficient associated with each individual variable in X indicates whether it is a significant predictor of AS-CTE course-taking. For this question, this model was run separately for each of the ELS and HSLS subsamples. To explore AS-CTE unit accumulation as a continuous outcome, we then employed a linear regression model. We performed these logit and linear regressions to respond to each of our subsequent research questions as well.


Research Question 2


We were also interested in how AS-CTE course-taking differed across the newer and older nationally representative cohorts of high school students. To do this, we combined the samples into a common dataset, and included a variable identifying membership in HSLS. This strategy replicates Bassok and Latham (2017), who compared patterns between two different national datasets of kindergartners. The following logistic regression equation explores changes in AS-CTE participation between cohorts:


[39_23035.htm_g/00010.jpg]


In this model, the [39_23035.htm_g/00012.jpg] identifies whether a respondent was a member of either the ELS or HSLS sample, with a 1 indicating membership in HSLS, and 0 indicating membership in ELS.


Research Question 3


Our final model focused on how membership in HSLS may have been differentially predictive of AS-CTE participation over time for female students and students with disabilities. To obtain these results, we interacted a binary variable identifying membership in ELS or HSLS with the key variables of gender and disability status. The following logistic regression equation was utilized:


[39_23035.htm_g/00014.jpg]


The interaction term [39_23035.htm_g/00016.jpg] identifies whether membership in HSLS is associated with any additional gain or loss in participation for female students, while the interaction term [39_23035.htm_g/00018.jpg] identifies whether students with disabilities experienced any additional gain or loss in odds of participation through membership in the HSLS sample. For the purposes of this study, student disability status was coded as a binary variable with a 1 indicating the presence of an IEP as identified by the school and 0 indicating no IEP.


Test of Robustness


Considering only approximately 50% of the sample in each of the ELS and HSLS datasets participated in AS-CTE, there is a strong possibility that our estimates are biased by the large number of students who did not accumulate any AS-CTE credits. In order to account for this issue, we employed a double hurdle model. This type of estimation is useful for count or continuous variables that have large numbers of observations with a zero as an outcome—in this case those who completed zero AS-CTE units. A double hurdle model, in this instance, allowed us to take advantage of the fact that while there were a large number of students who did not participate at all in AS-CTE coursework, they might have participated in these courses under different circumstances.


Using a double-hurdle model, two equations are estimated . The first equation determines whether a given observation is a zero type, where a zero type is defined as an individual that would not be expected to participate in AS-CTE under any circumstances . This first equation essentially assumes that there were a number of individuals who did not participate in AS-CTE but had a strong propensity to do so. The second estimation is used to then determine the overall AS-CTE unit completion provided an individual is not a zero type as determined under the estimation from the first equation. This will allow us to obtain a more accurate estimation of AS-CTE unit completion.


Considering the potential for wide differences in CTE policy across states, we were concerned that there may be impacts on our outcomes of interest at the state level. To account for any potential differences across states, we examined the results from a state fixed-effects model. Our findings from this model were nearly identical to our original estimates, implying that the changes in CTE course patterns should be discussed on a national level rather than within specific states. Therefore, our results below are presented using our data at the national level.


RESULTS


ELS AND HSLS COHORTS


AS-CTE Participation


Our first research question sought to identify which key variables predicted participating in AS-CTE courses in high school in ELS and HSLS samples separately. Table 2 presents these findings. The first model shows results for the ELS cohort, while the second model presents results for the HSLS cohort. For logistic models (1) and (2), coefficients are reported as odds ratios. A coefficient above 1 indicates higher odds, while a coefficient below 1 indicates lower odds.


Table 2. Factors Predicting Participation and Unit Completion in AS-CTE by Individual Cohort 

 

(1)

 

(2)

 

(3)

 

(4)

 

(5)

 

(6)

 

Odds Ratios for AS Participation

 

Applied STEM Unit Completion

 

ELS

 

HSLS

 

ELS

 

ELS Hurdle

 

HSLS

 

HSLS Hurdle

Student Level

           

Gender (Female)

 0.51***

 

 0.59***

 

–0.31***

 

–0.64***

 

–0.35***

 

–0.58***

 

(0.02)     

 

(0.02)

 

(0.02)

 

(0.03)

 

(0.02)

 

(0.02)

Race/Ethnicity

           

Black

 1.12

 

 1.02

 

 0.04

 

 0.09

 

–0.04

 

–0.03

 

(0.10)

 

(0.05)

 

(0.03)

 

(0.06)

 

(0.02)

 

(0.04)

Asian

 1.13

 

 0.92

 

 0.07

 

 0.17**

 

–0.02

 

–0.01

 

(0.08)

 

(0.05)

 

(0.04)

 

(0.06)

 

(0.02)

 

(0.04)

Hispanic

 1.11

 

 1.00

 

 0.00

 

 0.07

 

–0.05*

 

–0.06

 

(0.07)

 

(0.04)

 

(0.02)

 

(0.05)

 

(0.02)

 

(0.04)

Other

 1.26

 

 0.99

 

 0.08

 

 0.05

 

–0.01

 

–0.03

 

(0.23)

 

(0.05)

 

(0.07)

 

(0.15)

 

(0.02)

 

(0.04)

Socioeconomic Status

 0.95**

 

 0.98

 

–0.04***

 

–0.06***

 

–0.01

 

–0.01

 

(0.02)

 

(0.01)

 

(0.01)

 

(0.01)

 

(0.01)

 

(0.01)

Family Arrangement

           

Single parent

 0.96

 

 0.96

 

–0.01

 

 0.00

 

–0.03

 

–0.04

 

(0.04)

 

(0.03)

 

(0.02)

 

(0.04)

 

(0.02)

 

(0.03)

Other arrangement

 0.93

 

 1.00

 

–0.01

 

 0.01

 

 0.02

 

 0.02

 

(0.05)

 

(0.04)

 

(0.02)

 

(0.04)

 

(0.02)

 

(0.04)

Academic History and Attitudes

          

Ninth-grade GPA

 1.00

 

 1.04

 

 0.03*

 

 0.08**

 

 0.04**

 

 0.08***

 

(0.03)

 

(0.02)

 

(0.01)

 

(0.02)

 

(0.01)

 

(0.02)

Math score (% correct)

 0.83

 

 0.69*

 

 0.10

 

–0.02

 

 0.07

 

 0.01

 

(0.13)

 

(0.11)

 

(0.06)

 

(0.13)

 

(0.05)

 

(0.10)

Academic units

 1.03***

 

 1.04***

 

–0.01***

 

–0.02***

 

0.02***

 

–0.01*

 

(0.00)

 

(0.01)

 

(0.00)

 

(0.00)

 

(0.00)

 

(0.00)

Non-AS-CTE units

 0.97

 

 1.01

 

–0.01

 

–0.04***

 

–0.01*

 

–0.03***

 

(0.01)

 

(0.01)

 

(0.01)

 

(0.01)

 

(0.00)

 

(0.01)

Math self-efficacy

 1.00

 

 1.01

 

 0.00

 

 0.00

 

 0.01*

 

 0.01*

 

(0.01)

 

(0.01)

 

(0.00)

 

(0.01)

 

(0.00)

 

(0.00)

IEP

 0.84*

 

 0.94

 

–0.04

 

–0.06

 

 0.01

 

 0.01

 

(0.07)

 

(0.05)

 

(0.03)

 

(0.08)

 

(0.02)

 

(0.04)

Education Expectations

           

Two-year degree

 1.26**

 

 0.93

 

 0.10**

 

 0.19**

 

–0.02

 

–0.02

 

(0.09)

 

(0.07)

 

(0.03)

 

(0.06)

 

(0.03)

 

(0.06)

Four-year degree  

 1.10

 

 0.95

 

 0.02

 

 0.05

 

–0.04*

 

–0.05

               or more

(0.07)

 

(0.03)

 

(0.02)

 

(0.05)

 

(0.02)

 

(0.03)

 


Percent ELL

 0.99

 

 1.00

 

–0.01*

 

–0.01***

 

–0.00*

 

   0.00**

  

(0.01)

 

(0.00)

 

(0.00)

 

(0.00)

 

(0.00)

 

   (0.03)

 

Percent minorities

 1.00

 

 1.00*

 

 0.00

 

 0.00*

 

–0.00*

 

   0.00***

  

(0.00)

 

(0.00)

 

(0.00)

 

(0.00)

 

(0.00)

 

   (0.00)

 

Percent FRL

 1.00

 

 1.00

 

 0.00*

 

 0.00***

 

 0.00***

 

   0.01***

  

(0.00)

 

(0.00)

 

(0.00)

 

(0.00)

 

(0.00)

 

   (0.00)

 

Urbanicity

           
 

Urban

 1.14

 

 1.04

 

 0.00

 

 0.00

 

–0.04

 

   –0.04

  

(0.13)

 

(0.04)

 

(0.02)

 

(0.03)

 

(0.03)

 

   (0.03)

 

Rural

 1.11

 

 1.03

 

 0.02

 

 0.03

 

–0.01

 

   0.00

  

(0.14)

 

(0.04)

 

(0.02)

 

(0.04)

 

(0.02)

 

   (0.03)

 

Geographic Location

           
 

West

 0.95

 

 0.74*

 

–0.16*

 

–0.35***

 

–0.13**

 

   –0.33***

  

(0.15)

 

(0.11)

 

(0.07)

 

(0.05)

 

(0.05)

 

   (0.04)

 

South

 0.74***

 

 0.90

 

–0.13

 

–0.23***

 

 0.10*

 

   –0.16***

  

(0.11)

 

(0.04)

 

(0.07)

 

(0.04)

 

(0.05)

 

   (0.03)

 

Midwest

 1.18

 

 0.86

 

–0.08

 

–0.14**

 

–0.09*

 

   –0.18***

  

(0.18)

 

(0.12)

 

(0.07)

 

(0.04)

 

(0.04)

 

   (0.03)

             
 

N

14,800

 

21,900

 

14,800

 

14,800

 

21,900

 

21,900

 

Note. Robust standard errors adjusted for school clustering presented in parentheses. * p < .05. ** p < .01. *** p < .001.


In the ELS sample, there were only a few student-level variables associated with higher odds of taking AS-CTE courses. First, academic credit completion predicted slightly higher odds of AS-CTE participation (1.04, p < .00). Additionally, students who had college expectations at the two-year level (1.22, p < .01) had higher odds of taking AS-CTE courses compared to those without college expectations. There were also a number of student-level variables that negatively predicted AS-CTE course-taking. The most notable—and related to our final research question—was that female students (0.52, p < .001) had much lower odds of taking AS-CTE courses compared to male students. Students with an IEP (0.83, p < .01) also had lower odds of taking AS-CTE courses compared to students without IEPs. Finally, higher levels of socio-economic status predicted lower odds of AS-CTE participation.


Unlike in the ELS sample, neither IEP, socioeconomic status, nor college expectations predicted higher or lower odds of AS-CTE participation in the HSLS sample. Also unlike ELS, having higher math scores predicted lower odds (0.69, p < .05) of AS-CTE participation. Students in the HSLS sample also exhibited a similar pattern as compared to the ELS sample with respect to female students in that they were significantly less likely to participate in AS-CTE (0.59, p < .001).


AS-CTE Unit Completion


With respect to unit completion in models (3) through (6), coefficients can be interpreted as the number of additional units for a one-unit increase in the variable of interest. Models (3) and (5) represent the results from a traditional OLS estimation, while models (4) and (6) present the results of our double-hurdle estimations. Considering a double-hurdle model can provide for more accurate estimations in responding to our research question, we only discuss the hurdle model results in the text here—though OLS results are presented in Table 2.


In the ELS cohort, several variables predicted increased AS-CTE unit completion. For instance, Asian students took more AS-CTE units (0.17, p < .01). Additionally, students with higher ninth-grade GPAs (0.08, p < .01), and those with two-year college expectations (0.19, p < .01) compared to no college expectations, took more AS-CTE units. Female students (–0.64, p < .001) took fewer AS-CTE units than males, which corresponds to the lower odds of AS-CTE participation in the ELS cohort as discussed above. Similarly, socioeconomic status was also associated with completing fewer units of AS-CTE (–0.06, p < .001). On the other hand, while increased academic unit completion was related to higher odds of taking AS-CTE courses, it was negatively related to the number of units a student completed in AS-CTE (–0.02, p < .001). In other words, students with more academic units were likely to take an AS-CTE course, but not very many of them. Finally, the more units of non-AS-CTE a student completed predicted completion of fewer units in AS-CTE (–0.04, p < .001).


In the HSLS sample, similar to the ELS cohort, female students (–0.58, p < .001) completed fewer AS-CTE units compared to males as shown in the sixth model. Also similar to the ELS cohort, each unit of academic unit completion (–0.01, p < .05) and unit of non-AS-CTE unit completion (–0.03, p < .001) predicted taking fewer AS-CTE units in the HSLS sample. Unlike the ELS cohort, race/ethnicity, socioeconomic status, and college expectations were not associated with AS-CTE unit completion. Unique to the HSLS sample and unrelated to AS-CTE participation, math self-efficacy (0.01, p < .05) predicted slightly more units completed in AS-CTE courses.


COMPARING ACROSS COHORTS


AS-CTE Participation and Unit Completion


Our second research question asked how the profile of AS-CTE course-taking changed before and after the authorization of Perkins IV. To respond to this question, we examined ELS and HSLS together in a single model with membership in the HSLS cohort serving as the predictor of interest. All other variables from the previous models were again included as controls. Table 3 presents the results of our cross-cohort analyses.

             
  
 

Table 3. Results of Combined Sample Analyses Predicting Participation and Unit Completion in AS-CTE

  

(1)

 

(2)

 

(3)

 

(4)

 

(5)

 

(6)

  

Applied STEM CTE Participation

 

Applied STEM Unit Completion

 

Applied STEM Unit Completion – Hurdle

HSLS

 1.30**

 

 1.15*

 

 0.09***

 

 0.10**

 

 0.23***

 

 0.15***

 

(0.08)

 

(0.08)

 

(0.02)

 

(0.03)

 

(0.02)

 

(0.03)

Female students

 0.56***

 

 0.50***

 

–0.33***

 

–0.32***

 

–0.60***

 

-0.65***

 

(0.02)

 

(0.02)

 

(0.01)

 

(0.02)

 

(0.02)

 

(0.03)

IEP students

 0.91*

 

 0.81**

 

–0.01

 

–0.03

 

–0.01

 

–0.06

 

(0.04)

 

(0.06)

 

(0.02)

 

(0.03)

 

(0.04)

 

(0.06)

Female students*HSLS

  

 1.20**

   

–0.03

   

 0.06

   

(0.08)

   

(0.02)

   

(0.04)

IEP students*HSLS

  

 1.21**

   

 0.03

   

 0.07

   

(0.10)

   

(0.04)

   

(0.07)

             
 

N

36,700

 

36,700

 

36,700

 

36,700

 

36,700

 

36,700

 

Note. Robust standard errors adjusted for school clustering presented in parentheses.

* p < .05. ** p < .01. *** p < .001.

     

The first model shows students in the HSLS cohort (1.30, p < .001) had significantly higher odds of taking AS-CTE coursework than students in the ELS sample. The more recent cohort of students in HSLS had 30% greater odds of participating in AS-CTE courses, as identified by enrollment in at least one AS-CTE course, than did students in the ELS sample. In other words, students were more likely to take AS-CTE courses in the high school class of 2013 (HSLS) compared to the high school class of 2004 (ELS), after controlling for a wide range of additional factors.


We also tested whether there was a similar pattern with regard to AS-CTE unit completion; the results are presented in the third and fifth models. Again, for the sake of parsimony, we only discuss the results of the hurdle model (model five) here. We once again found membership in HSLS (0.23, p < .001) to have a significant, positive relationship with unit completion. That is to say, students in the high school class of 2013 (HSLS) were taking an additional 0.23 unit in AS-CTE compared to students in the high school class of 2002 (ELS). This translates to approximately one quarter of a year more coursework in AS-CTE.


AS-CTE for Females and Students With IEPs


Considering one emphasis of the most recent reauthorization of the Perkins Act was to increase participation in CTE (AS-CTE in particular) for key subsets of students and the persisting gender and disability gap in later STEM outcomes (e.g., STEM major or STEM career), our final research question asked how AS-CTE course-taking and unit completion had changed over time for female students and students with IEPs. In this case, we were most interested in the interaction between HSLS and the variables identifying student gender and IEP status. Predicting AS-CTE participation shown in the second model in Table 3, we found a positive, significant interaction term between HSLS and gender (1.20, p < .01), indicating that female students in the HSLS sample had 20% greater odds of taking AS-CTE courses in the HSLS sample than females in the ELS sample. This means that on the aggregate, female students in the class of 2013 (HSLS) were more likely to take an AS-CTE course than female students in the class of 2004 (ELS).


Similarly, there was a positive, significant interaction between HSLS and IEP status (1.21, p < .01).  Students with IEPs in HSLS had 21% greater odds of AS-CTE participation than similar students in ELS. Again, interpreting this aggregately, students with IEPs in the high school class of 2013 (HSLS) were more likely to take an AS-CTE course than students with IEPs in the class of 2004 (ELS). That is, more students with IEPs have been taking AS-CTE courses in more recent years.


We also looked at differences in unit completion for both female students and students with disabilities across the two cohorts, presented in the fourth and sixth models in Table 3. Despite observed overall increased participation as seen above, there were no differences in unit completion in either models. Both female students and students with IEPs in HSLS were predicted to complete numbers of units in AS-CTE statistically comparable to their ELS counterparts. This means that while students in the more recent high school class of 2013 (HSLS) were more likely to take an AS-CTE course, these students were not necessarily taking more units. That is, more female students and students with disabilities were taking AS-CTE classes, but more students overall were not taking more of them.


DISCUSSION


In recent years, research has showcased the benefits of AS-CTE course-taking in high school on future outcomes. Prior work has linked taking these courses to improved high school achievement, higher odds of taking more advanced math and science courses, a greater likelihood of selecting a STEM major in college, improved chances that students apply to and attend college, improved chances of securing employment immediately out of high school, and higher salaries (Bozick & Dalton, 2013; Dougherty, 2016; Gottfried, 2015; Gottfried et al., 2014; Gottfried & Sublett, 2017; Plasman & Gottfried, 2016). Moreover, female students who took just one engineering-focused AS-CTE course in high school were more likely to pursue undergraduate and graduate degrees in engineering than males who took just one AS-CTE course (Gottfried & Plasman, 2018). Additionally, students with IEPs who took an AS-CTE course had improved high school and college-going outcomes (Sublett & Gottfried, 2017; Plasman & Gottfried, 2016). Therefore, there is recent evidence that high school AS-CTE course-taking can influence key STEM pipeline outcomes, especially for traditionally underrepresented groups in STEM fields.


To our knowledge, this is the first study to compare patterns of AS-CTE course-taking across two cohorts of nationally representative high school students—the graduating classes of 2004 (i.e., ELS) and 2013 (i.e., HSLS). Evaluating differences in AS-CTE course-taking patterns is noteworthy because these patterns potentially reflect significant changes in federal policy following the 2006 reauthorization of the Perkins CTE Improvement Act. Given that a key tenet of Perkins IV was to focus on more students taking CTE courses in high school, it behooves the field to determine if this has been happening. We do so specifically for AS-CTE courses, given the aforementioned established benefits as well as voiced concerns about the STEM labor market.


A key finding of this study is underscored when examining these nationally representative datasets in tandem—i.e., looking for changes across two cohorts separated by almost a decade with a Federal CTE reauthorization occurring in between them. Most importantly, relatively more students in HSLS were taking AS-CTE courses compared to those in ELS. That is, in the graduating high school class of 2013, students had greater odds of enrolling in at least one AS-CTE course compared to students in the class of 2004. Additionally, the average number of units in AS-CTE increased between cohorts. The high school graduating class of 2013 took one quarter of a year more units in AS-CTE compared to the graduating class of 2004—approximately an additional half a class, considering most classes are one semester long. We do not claim Perkins IV caused the increase in AS-CTE course-taking; for instance, over the same period there was a massive economic downturn that may have fueled more students pursuing STEM areas for labor market competitiveness , or perhaps less pressure from No Child Left Behind encouraged an increase in CTE course offerings. Nonetheless, the descriptive evidence here does suggest that students after the 2006 Perkins reauthorization were more likely to be enrolled in AS-CTE courses and were taking more of them.


We also examined the course-taking patterns for female students and students with IEPs specifically, given that these two groups generally have been underrepresented in STEM areas (Gottfried & Plasman, 2018; Plasman & Gottfried, 2016). In the separate analyses of ELS and HSLS cohorts, females and/or students with IEPs had lower odds of taking AS-CTE courses and took fewer units compared to their respective counterparts. But the news is not entirely grim, as exemplified when conducting our cross-cohort analysis. Female students and students with IEPs had higher odds of taking AS-CTE courses in HSLS than they did in ELS. In other words, the relative course-taking gap—while still present in each individual cohort—is shrinking when looking across time. As another interpretation, exposure to AS-CTE courses has increased. But we did not see differences in AS-CTE unit accumulation for females and students with IEPs across cohorts. Interpreting these together, then, gaps might be shrinking in one domain—i.e., participation—but on the other hand, more females and students with IEPs are not necessarily taking more units. Perhaps this yields evidence of breadth, but not necessarily depth, in the current AS-CTE profile of course-takers.


A key consideration in understanding AS-CTE differences focuses on whether there have been changes in the types of courses in which students are participating in the area of AS-CTE. In the ELS dataset, there were three specific classes in which students enrolled most often: computer appreciation, computer applications, and desktop computer application suites. In the more recent HSLS dataset, two of the most popular courses were similarly focused: computer applications and business computer applications. However, the next two most popular courses were much more applicable in nature than was computer appreciation. In particular, students in HSLS were choosing to enroll in webpage design and drafting. This pivot in course enrollment away from computer appreciation to courses with practical applications may help to explain some of the growth in AS-CTE over time. The change in the focus of these courses also provides evidence that the most recent Perkins Act reauthorization is helping to promote the overall relevance of CTE coursework.


IMPLICATIONS FOR POLICY AND PRACTICE


Given these findings, there are several concluding implications. First, it appears that national efforts and sentiments around improving high schoolers’ participation in CTE course-taking have taken hold, given that students in the graduating class of 2013 were more likely to enroll in AS-CTE courses and take more course units. With the potential reauthorization of the Perkins Act currently being discussed in Congress as of 2018, it appears that continuing to increase students’ access to CTE—and AS-CTE in particular—courses remains a national educational priority as we move into the next decade. As ours is the only known study examining changes in patterns across multiple national cohorts with regard to AS-CTE, we would urge federal policymakers to use the findings from this study and future studies to consider whether the increases in AS-CTE course-taking are meeting or exceeding policy expectations with regard to the national profile of high school course-takers prior to any reauthorization.


For instance, if the goal of Perkins in its current form is to increase the rigor of AS-CTE coursework so that students are college and career ready and also to expose as many students as possible to AS-CTE, then the policy appears to be moving in the correct direction, given that we found a broadening of participation between cohorts (in conjunction with previous literature on the benefits of taking even just one AS-CTE course in high school). However, as more students participate in these courses, it might be the case that there is a crowding out of students who were traditionally exposed to in-depth, career-related STEM training in high school. Perhaps, in this scenario, policy goals would need to be readjusted if number of units taken remains a priority in addition to broadening the participation of course-takers. Therefore, the question remains whether the patterns of increased participation and number of courses/units taken in AS-CTE between high school cohorts of 2004 and 2013 would be viewed as a success by the policymakers who reauthorized Perkins in 2006. If the goal is to increase visibility and participation specifically in AS-CTE, it would appear Perkins IV is helping to make useful contributions. However, if the goal is to expand all CTE in general, policymakers may want to consider how to better further this cause.


The second implication of our findings is that, aside from national policymaking efforts to boost participation in CTE areas, there is a clear role for states. Over the past decade, there have been numerous changes to state graduation requirements to incorporate AS-CTE. For example, in 2013 Wisconsin allowed students to earn math and science credits through CTE courses. Texas, Arkansas, and West Virginia require schools to offer computer science courses. And in late 2017, Virginia became the first state to require that all students receive computer science instruction. Hence, states have the potential to contribute to increasing students’ participation in AS-CTE via high school requirements and course offerings. It appears that since 2006, both the state and federal policymakers have well-aligned interests in regard to boosting enrollment in AS-CTE courses. With a pending reauthorization, further efforts might be considered to continue to align policy efforts across multiple stakeholder levels, such that participation and pursuit persist.


Finally, more attention in both research and policy needs to continue addressing course-taking gaps experienced by underrepresented groups, such as by gender and disability. As noted, the findings from this study are not grim—participation gaps might be closing as relatively more females and more students with IEPs were taking AS-CTE courses in the 2013 graduating class than they were almost a decade earlier. And as mentioned above, prior research has found that even when females or students with IEPs take a single AS-CTE course, there are positive benefits to the education pipeline (i.e., applying to college) as well as the STEM pipeline (i.e., math achievement, more likely to major in STEM, more likely to pursue graduate degrees in STEM) (Gottfried & Plasman, 2018; Gottfried & Sublett, 2017). Given these benefits, federal and state efforts encouraging students from traditionally underrepresented groups to take AS-CTE courses are merited. The question, however, is why females and students with IEPs are not pursuing more units in these high school courses. Why is it that they are taking only one course, for instance? What are the experiences in these courses like? While this study cannot answer these questions, the issue remains as to why more females and students with IEPs might be taking these courses, but not taking more courses.


In conclusion, in the context of a growing emphasis on CTE course-taking at the federal level via Perkins IV and states’ efforts to improve participation in AS-CTE courses via changing graduation requirements, we are indeed witnessing more students in the United States take AS-CTE courses, and take more of these courses on average. Furthermore, with continued efforts at broadening the participation of traditionally underrepresented groups in STEM areas, we do see relatively more females and students with IEPs taking these courses. As our nation continues to expend effort and energy to improve college and career readiness for all students, our examination of high schoolers over a decade shows that AS-CTE might be one way to improve participation in coursework that promotes desired pipeline outcomes.  


LIMITATIONS


While this study was able to examine AS-CTE course-taking patterns for each student in the dataset, it was not possible to examine students’ perspectives on these courses. In other words, no data exist in either dataset on why students chose to take AS-CTE courses. Future research might consider a smaller-scale survey that asks students why they chose to take certain courses. This might also provide insight into gender and IEP gaps in number of units taken. Second, the methods in this study are correlational and descriptive. Therefore, we cannot directly attribute any changes in AS-CTE participation to federal or state policies. Future research might consider a quasi-experimental design, perhaps using the rollout of courses observed in longitudinal statewide data to examine how the implementation of particular policies impacts participation outcomes. Finally, the datasets do not contain detail on the teachers instructing these courses. A follow-up study with appropriate data might consider if there have been changes to the teacher workforce. Doing so would continue to provide a richer portrait regarding the changing landscape of AS-CTE coursework.


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APPENDIX A


SELECTED AS-CTE COURSES FROM ELS AND HSLS


ELS

HSLS


IT

Desktop Computer Application Suites   

Computer Applications

Computer Appreciation

Data Processing

Intro to Internet and the World Wide Web

   

ET

Computer Assisted Design/Drafting

Mechanical Drawing 1

Mechanical Drawing 2

Industrial Production Technology 1

Engineering Drawing 1


IT

Database Management and Data Warehousing

Business Computer Applications

Web Page Design

Geospatial Technology

Networking Systems


ET

Wind Energy

Laser/Fiber Optics

Aerospace Engineering

CAD Design and Software
Biotechnical Engineering





Cite This Article as: Teachers College Record Volume 122 Number 2, 2020, p. 1-28
https://www.tcrecord.org ID Number: 23035, Date Accessed: 10/21/2021 11:52:20 PM

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