The impact of school-to-work and career and technical education in the United States: evidence from the national longitudinal survey of youth, 1997

Journal of Vocational Education & Training ISSN: 1363-6820 (Print) 1747-5090 (Online) Journal homepage: http://www.tandfonline.com/loi/rjve20 The impact of school-to-work and career and technical education in the United States: evidence from the national longitudinal survey of youth, 1997 James R. Stone III To cite this article: James R. Stone III (2002) The impact of school-to-work and career and technical education in the United States: evidence from the national longitudinal survey of youth, 1997, Journal of Vocational Education & Training, 54:4, 533-582 To link to this article: http://dx.doi.org/10.1080/13636820200200213 Published online: 20 Dec 2006. Submit your article to this journal Article views: 125 View related articles Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalinformation?journalcode=rjve20 Download by: [148.251.235.206] Date: 12 February 2016, At: 13:32

Journal of Vocational Education and Training, Volume 54, Number 4, 2002 The Impact of School-to-Work and Career and Technical Education in the United States: evidence from the National Longitudinal Survey of Youth, 1997 JAMES R. STONE III University of Minnesota, Saint Paul, USA ABTRACT This study explores changes in school-to-work and career and technical education participation between the1996-97 and 1998-99 school years in the United States. Employing data from the National Longitudinal Survey of Youth 1997, the author focused on changes in student characteristics. The study concludes that there are fewer differences between participants in school-to-work in the two years after the initial data collection. There also appears to be a trend toward less participation at each grade level in activities identified as school-to-work although there has been an increase in the proportion of students identified as a career and technical education (CTE) concentrator. Background There has been much school reform during the last 15 years designed to address the concerns and perceived shortcomings of the United States education system and to prepare students more fully for educational and economic attainment. The first of such reforms took place in the 1980s and focused attention on these perceived shortcoming and how, if left unattended, they would lead to generations of under prepared students who would then enter a globalized and highly competitive workforce. This led to back to basics approaches characterized by requiring more academic credits for graduation. The next wave of reform, often described as restructuring prescribed specific reform strategies like school choice, scheduling changes and site-based management (Michaels, 1988). The most recent wave of reform integrates academic rigor with work preparation work-based learning (Castellano et al, 2001). 533

James R. Stone III One component of these reform waves is building stronger links between secondary and post-secondary education. This has become a key strategy for improving student achievement and attainment, especially in CTE. Specific initiatives such as tech prep, the passage of the 1984, 1992 and 1998 Carl D. Perkins CTE legislation, and the 1994 Schoolto-Work Opportunities Act have emphasized this link and its presumed outcomes. These three strands of legislative initiatives are discussed below. The Perkins Acts II & III The 1990 Carl D. Perkins Vocational and Applied Technology Education Act ( Perkins II ) mandated that federally-funded CTE programs develop the following: tech prep programs; integration of vocational and academic curricula; promotion of work-related experience; and accountability for funding continuation. Under this mandated reform, resources were directed towards special populations, which included those considered poor, persons with disabilities, and limited English-speaking individuals. This emphasis was historically consistent with the belief that vocational education served students deemed not likely to finish high school or not expected to continue formal education beyond high school. Tech prep. Parnell (1985) argued that most students left high school unprepared for either work or further education. His solution was a four year program of study that incorporated the last two years of high school and two years of post-high school education and training. This 2 + 2 model, targeted at the middle range of high school students, would focus on preparing them for increasingly sophisticated jobs requiring technological skills. In this plan, students would build on a foundation of academics in applied settings, such as math, science, and communications and follow with a sequence of technical courses coordinated with post-secondary institutions. Curriculum integration. The notion of curriculum integration goes back almost a full century to John Dewey (1916). While calling for curriculum integration, Perkins II purposefully failed to define it beyond the identification of a set of coherent course sequence that was intended to lead to student achievement of academic and vocational competencies. 534

CHANGES IN SCHOOL-TO-WORK PARTICIPATION Work-related experience. Work-related experience or work-based learning has long been a feature of CTE. Its inclusion in Perkins II emphasized the importance of coordinating and sequencing with school-based learning. The legislation highlighted its importance in improving the transition from school to work. Accountability. Before Perkins II, there was little accountability required of states save reporting enrollment numbers and policy compliance (Hoachlander, 1995). Now states were told to develop performance measures, to determine standards for those measures, and evaluate based on those standards. The legislation identified six outcomes: enrollment, academic skills, occupational skills, school completion, job placement, and wages or job retention (Stecher et al, 1998). Although mandated in the legislation, states were slow to implement. The Carl D. Perkins Vocational and Technical Education Act Amendments of 1998 (Public Law 105-332), also know as Perkins III, was enacted in 1998 and sought to cast vocational education, now called career and technical education within the broader context of education reform. It supported the alignment of CTE with state and local reform efforts. In a move to change the perception of secondary CTE, the Perkins II targeted special population s funding was eliminated. Consistent with the tenor of the times, this legislation offered states more flexibility to allow for experimentation and new program development. Perkins money, for example, could now be used to support CTE in charter schools. Building on Perkins II, Perkins III expanded the goals of CTE to include: Integrate academic and vocational education. Promote student attainment of challenging academic and vocational standards. Provide students with strong experience in, and understanding of, all aspects of an industry. Involve parents and employers. Provide strong connections between secondary and post-secondary education. Develop, improve, and expand the use of technology. Provide professional development for teachers, counselors and administrators. (Bailey & Kienzl, 1999) Support for Tech Prep programs continued but expanded to allow funds to support linking 4-year institutions with community colleges and high schools in tech prep sequences, a 2+2+2 concept. Perkins III continued supporting work-based learning experiences. And, there was increased emphasis on professional development for teachers and counselors. 535

James R. Stone III Perhaps the biggest change was the increased emphasis on accountability. Four new core indicators of performance for federally funded secondary and post-secondary CTE programs were established. They addressed student achievement, credential acquisition, transition to and completion of post-secondary education or advanced training, the military or employment; and non-traditional training and employment. In a new wrinkle for federal legislation, states are required not only to identify indicators that measure performance on the four core indicators, but also to demonstrate annual student performance improvement on these four indicators. Failure to do so could result in the loss of funding. School-to-Work While it is now a cliché to say that we live in a global economy, this fact with all of its attendant implications has profoundly altered the nature of the skills sets required for success in today s workplace. Against a backdrop of concern over declining US economic fortunes in the late 1980s came the School-to-Work Opportunities Act (STWOA). Passed in 1994, the STWOA was designed to encourage states to create more coherent systems that would bridge the gap between work and education for all students, not the select few who aspired to a narrow range of professional careers that offered transparent pathways. Building on the belief that students learn best by doing and then applying what they learn in school to the work place, the Act funded school-employer partnerships to design and implement work-based learning programs as defined in the Act (Levesque et al, 2000). In a report by Hughes et al (2001) it was consistently shown that students enrolled in school-to-work (STW) are less likely to drop out of school, have better attendance and graduate on time. This they argued occurred while these students benefited from career development opportunities, developing work related skills and were moving along their path towards their career passion. It is useful to note that the law draws no distinction between pedagogy (e.g. school-based enterprises, cooperative education), programs (e.g. Tech Prep) or pedagogies wrapped in programs (e.g. apprenticeships). The following elements were defined in the STWOA (National School-to-Work Office, 1996): Apprenticeships. Registered apprenticeships are relationships between an employer and employee during which the paid worker, or apprentice, learns an occupation in a structured program jointly sponsored by employers and labor unions. Youth Apprenticeships, on the other hand are typically a multi-year program combining school and work-based learning in a specific occupational cluster and are designed to lead directly into either a related post-secondary program, or a registered 536

CHANGES IN SCHOOL-TO-WORK PARTICIPATION apprenticeship. Unlike registered apprenticeships, Youth Apprenticeships may or may not include financial compensation. Cooperative education is a structured method of instruction whereby students alternate or coordinate their high school or post-secondary studies with a job in a field related to their academic or occupational objectives. Written training and evaluation plans guide instruction and students receive course credit for both their work and classroom experiences. Internships are situations where students work a specified period of time for an employer to learn about a particular industry or occupation. Workplace activities may include sample tasks across different business units, special projects, or may focus on a single occupation. These positions may or may not include financial compensation. Job shadowing is a part of career exploration activities in middle and early high school. Students follow an employee in a work setting for one or more days to learn about a particular occupation or industry. School-based enterprises are defined in the Act as an enterprise in which goods, or services are produced by students as part of their school program. Stern et al (1994) refine this definition by focusing on production of goods and services for sale or use by people other than the students involved. Tech Prep identifies programs that offer at least four years of sequential course work at the secondary and post-secondary levels to prepare students for technical careers. These programs typically begin in 11th grade and are designed to conclude with the award of an associate s degree or certificate after two years of post-secondary education or training. By design, Tech Prep is intended to build student competency in academic and technical subject matter. Career major or Career pathways are defined in the STWOA as a coherent sequence of courses or field of study that prepares a student for a first job. These feature many of the same elements as Tech Prep and Youth Apprenticeship (integrated curriculum, work-based learning, secondarypost-secondary linkages) but may also include linkages to four-year colleges or universities. Pucel (2001) offers a slightly more expansive view that describes career majors as combinations of existing courses within a high school focused on particular career clusters without the accoutrements of the other programmatic initiatives. These Career Major Pathways have become a popular tool to help schools organize 537

James R. Stone III curriculum and faculty to create greater coherence in the otherwise random collection of courses typical of today s high schools. Today s High School During the decade of the 1990s, many reform efforts were aimed at improving the lot of secondary students. Many of these are described in detail in the Castellano et al (2001) report on school reform and disadvantaged students. While federal legislation has sought to influence the availability of CTE and STW for all students, high schools have their own internal logic. Despite years of reform efforts, most high schools still have a pathway for the academically gifted students and for those students thought headed to early labor market entry. The rest of the students are left to wander haphazardly through the high school years. Thus we have the academic track, the vocational track and the general track. One result of the several CTE/STW reform efforts is the beginnings of a fourth track, one comprised of students who follow a rigorous academic sequence of courses and a rigorous sequence of vocational courses. These dual concentrators while small in numbers, may represent the natural conclusion of these reform efforts. What follows is an overview of current research on how these tracks or curricular patterns relate to achievement and academic course taking. We conclude this section with a discussion of the impact of employment while in high school on academic achievement. Curricular Patterns and Achievement The 1998 High School Transcript Study (Roey et al, 2001) illustrated how high school students are sorted as a function of curriculum patterns. These data show that 71.0% were enrolled in an academic high school program, 4.4% were enrolled in a vocational program, 19.3% were enrolled in a program that included both types of courses (integrated curriculum) and 5.4% of the students were enrolled in a program that was considered neither academic or vocational (general). One notable course-taking change came under the category of Vocational Courses, whereby there was a dramatic increase in the number of students enrolled in Technical and Communications courses. The increase in students earning one or more credits in this area rose from 12.10% in 1990 to 24.14% in 1998. Levesque et al (2000) concluded that student GPA was associated with course-taking patterns. For instance, higher GPAs were associated with students who had completed more total coursework. Higher GPAs were also associated with more academic course work. Conversely, those students who completed more vocational credits had lower GPAs. 538

CHANGES IN SCHOOL-TO-WORK PARTICIPATION Drawing somewhat different conclusions, Roey et al (2001) reported that overall student GPA is related to National Assessment of Educational Progress (NAEP) proficiency rather than particular courses taken. They found that GPA correlation coefficients were 0.552 with reading proficiency, 0.559 with writing and 0.540 with civics. There was no correlation between NAEP proficiency and absenteeism. According to Levesque et al (2000) the overall academic preparation of students participating in vocational education increased between 1982 and 1994; however, there are some additional changes worthy of note. For instance, there was an overall decline in vocational course-taking between 1982 and 1994, with a sharper decline for females over males and for Hispanics over other ethnic groups. Rural high school students completed more vocational coursework than did students from urban and suburban high schools. Private schools, overall, reported few vocational course offerings. However, students with disabilities increased their vocational course-taking during this same period of time. This latter finding is consistent with the emphasis on special populations in Perkins II and earlier vocational education legislation. Academic Course-Taking Across Curricular Patterns Delci & Stern (1999) provided an early analysis of students enrolled in four high school curricular patterns. These were identified as academic concentrators, vocational concentrators, dual concentrators, and no concentration. They reported that dual concentrators (students pursuing a combined academic and vocational program) reflected the overall population of schools in terms of income and ethnic composition although they were more likely to be female. Vocational concentrators are more likely to be from lower income families and of non-european heritage. Overall, those students who classified themselves as enrolled within an academic program take more Algebra, Geometry, and Algebra II (Delci & Stern, 1999) than other students. Vocational students are the least likely to take these courses. Those students in a combined academic and vocational program are similar to general students in having taken Geometry and Algebra II. Nonetheless, Delci & Stern reported that one out of five students in a combined program and one out of three in the vocational program had not taken Algebra, Geometry, or Algebra II by 11th grade. Science course-taking differs from math course-taking. For instance, the data from Delci & Stern (1999) show that students in the 11th grade for the academic program were more likely to have taken science courses, which include Biology and Chemistry, than students in the combined curriculum. Students in the vocational program reported taking more technical and applied science courses than students in other 539

James R. Stone III programs. According to transcript data vocational students were less likely to take science courses in 1982, 1990, 1994 (Roey et al, 2001). Employment and Educational Outcomes Engaging students in work-based learning is a hallmark of the STWOA. There has been much debate about the effect of adolescent employment on achievement and post-high school academic trajectories (see Stone & Mortimer, 1998). Stone & Mortimer and more recently, Warren et al (2000) argue that earlier studies reported in the literature do not show an accurate picture of the relationship between employment during high school and educational outcomes. Warren et al (2000) speculated that this relationship is far more complex and that research models are not adequately displaying this by assessing the reciprocal nature of employment and educational outcomes such as grades. They show that high school employment had neither long- nor short-term effects on grades in academic courses nor did grades influence employment. Nonetheless, there are consistent cultural differences in student employment activity. For example European-American students are more likely to be employed than Hispanic- and African-American students (Stone & Mortimer, 1998; Warren et al, 2000). There is a small, but growing body of evidence that the nature or quality of the work may affect outcomes of interest. (For an extended discussion of this, see Stone & Josiam, 2000; Stone & Mortimer, 1998). School supervision of the work experience (e.g. cooperative education) does affect the quality of the work adolescents obtain, but not the development of work-based attitudes and behaviors (Stone & Josiam, 2000). Federal legislation in the past three decades, has increasingly emphasized the links between secondary and post-secondary education and on related student academic and occupational outcomes reflecting these links (Bragg, 2000; Gray & Herr, 2000). Building stronger links between secondary and post-secondary education has become a key strategy for improving student achievement and attainment. Specific initiatives such as tech prep, the passage of the 1984, 1992 and 1998 Carl D. Perkins CTE legislation, and the 1994 School-to-Work Opportunities Act have emphasized this link and its presumed outcomes. While literature in the early 1990s indicated that vocational courses had a neutral effect (Rasinski & Pedlow, 1994) and/or academic courses had a more positive effect than vocational courses (McCormick et al, 1995), more recent literature begs the question of the actual impact of educational initiatives, like tech prep and school-to-work, on students transitions to post-secondary education and work. Many questions have emerged in regard to what affects students achievement and transition to post-secondary education and, ultimately, to the workplace. 540

CHANGES IN SCHOOL-TO-WORK PARTICIPATION Education that extends beyond traditional secondary education to community, junior, and technical colleges is a requirement in today s context of increased demands for skilled workers who could take their place in a more sophisticated, more technological work environment (Bragg & Layton, 1995). These blended workforce and educational demands served as groundwork for many educational initiatives in the 1990s that were oriented at promoting student transitions to postsecondary education and work. Today questions are being asked regarding the impact of these efforts. These questions frame much of the Perkins III legislation that led to the creation of the current National Research Center for Career and Technical Education. These questions still loom large as the current administration is asking about the worth and value of CTE in America s schools. Objectives of the Study 1. Describe school-to-work (STW) and career-technical education (CTE) students characteristics: How patterns of participation in the curricular patterns described as academic concentrator, CTE concentrator, dual concentrator, and general concentrator have changed between the 1996-97 and 1998-99 school years. How patterns of participation in STW, Career Pathways and CTE elements have changed between the 1996-97 and 1998-99 school years. 2. Predict STW and CTE participation: What characteristics of youth predict participation in curricular patterns in STW, CTE, and Career Pathways. How these predictive characteristics have changed between the 1996-97 and 1998-99 school years. 3. Examine attitudinal and behavioral patterns of STW/CTE students including educational and work expectations, school absences and tardiness, time use, and risky behaviors: What the relationship is between curricular patterns, STW/CTE participation and self-reports of GPA. What the relationship is between curricular patterns, STW/CTE participation and student graduation, college attendance and future work aspirations. What the relationship is between curricular patterns, STW/CTE participation and self-reports of school attendance and tardiness. What the relationship is between Curricular Patterns, STW/CTE participation and self-reports of risky personal behavior. 541

James R. Stone III Methodology Database The National Longitudinal Survey of Youth 1997 (NLSY97) is described by the Bureau of Labor Statistics (BLS) as a database that consists of a nationally representative sample of approximately 9,000 youth who were 12 to 16 years old as of December 31, 1996 that is designed to document the transition from school to work and into adulthood (Bureau of Labor Statistics, 2001a). Data from the first and second waves of interviews were used in these analyses. The first round of interviews took place in 1997 and the second round was done in 1998. Following BLS guidelines, we weighted the observations to estimate population parameters (Bureau of Labor Statistics, 2001b, p. 34). The weighted sample permits researchers to estimate how many individuals in the US are represented by each respondent. Variables For the purposes of these analyses, we created several measures of participation in STW and CTE designed to capture the possible participation configurations in one or more of the STW elements and Career Major or Pathways as defined by the US Department of Education (Bureau of Labor Statistics, 2001b). Four configurations of STW and Career Pathways were thus constructed. The first group included those who did not participate in any STW activities (i.e. job shadowing, cooperative education, internships, apprenticeships, school-based enterprise, or tech prep) and did not participate in a Career Pathway (No STW, No CP). The second group included those who participated in one or more STW element but did not participate in Career Pathway (STW, No CP). The third group included students who participated in one or more STW element and a Career Pathway (STW, CP). The fourth group included students who did not participate in a STW element but did participate in a Career Pathway (No STW, CP). Students also self-identified into one of four curricular patterns or Concentrations. These were identified as Academic Concentration, CTE Concentration, Dual Concentration, and General Concentration. Both of these variable sets were dummy coded. The omitted or comparison group for STW participation was the No STW, No CP group. The omitted group in the Curricular Concentration was the Academic group. Table coefficients for these variable sets are thus interpreted in comparison to the omitted group. 542

Participation in STW and CTE: 1997-1999 CHANGES IN SCHOOL-TO-WORK PARTICIPATION Several studies have examined STW participation in the United States. Each has come to similar but slightly different conclusions regarding the distribution of student participation in STW elements. These comparisons are shown in Table I along with our findings. These slight differences are likely attributable to the way in which survey respondents were classified for analyses. This is especially true for measures of Cooperative Education, Tech Prep, and Apprenticeship participation by 9th and 10th graders as will be discussed later. STW Programs Visher & Others (1998) Delci & Stern Bishop & (1999) [1] Others (2000) [2] Current study (2002) [3] Career 22 % 18.2 % 19 % 18.2 % Major/Pathway Tech Prep 17 % 7.6 % 8 % 7.6 % Coop Ed. 6 % 6.7 % 7.3 % 7.4 % (11th &12th grades) Job Shadow 12 % 12.5 % 12 % 12.6 % Mentoring 5 % 4.4 % 5 % 4.7 % School 7 % 8.9 % 9 % 9.1 % Enterprise Apprenticeship/ Internship 4 % 4.3 % 5 % 6.6 % (11th &12th grades) Visit Work Site - - 12 % - At least one - - 43 % - No STW programs - 69 % - 69.1 % [1] Analysis is based on 9th to 11th grade (n = 4254, Weighted n = 9,158,583) [2] Analysis is based on 9th to 12th grade and 12 to 16 years old in Dec 1996 [3] Analysis is based on 9th to 12th grade in Cohort I (n = 4469, Weighted n = 9,815,752) Table I. Participation Rate in School-to-Work Programs in NLSY97: comparison of studies. Our first objective was to describe STW/CTE students and identify the factors associated with participation in various forms of STW and CTE. We asked two questions. First, how has overall participation in STW and CTE changed in recent years? Secondly, has the relationship between participation in STW, Career Pathways and Curricular Concentration changed between Cohort I and Cohort II? The NLSY97 as of this writing has collected two rounds of data. They began their collection of adolescent interviews in the Spring of 1997 543

James R. Stone III capturing data from students in the 1996-97 school year. They began a second round in the fall of 1998, capturing data from students in the 1998-99 school year. This had the effect of creating two cohorts of 9th graders, 10th graders, 11th graders and 12th graders two academic years apart. There was some slippage in the BLS data collection process and a small number of students were interviewed during the 1997-98 school year (see Table II). Responses from these students were included in Cohort I. Month September October November December January February March April May June July August Cohort I 1996-97 Academic Year - - - - 14 1,236 1,622 1,838 1,737 1,173 534 363 1997-98 Academic Year 63 - - - - - 178 192 30 - - - Cohort II 1998-99 Academic Year - 104 2,713 2,094 1,498 912 389 16 - - - - Total 8,980 8,384 Table II. NLSY97 Youth interviews: month and academic year by cohort. Patterns of Participation in STW We examined self-reports of participation in the STW elements and found a modest but consistent drop in 1999 in the proportion of students reporting STW participation in all elements with respect to 1997, although absolute numbers have risen. This was accompanied by a corresponding rise in the proportion of students reporting No STW participation (Table III). Despite this drop, nearly four million of 14 million Cohort II students participated in one or more STW elements. 544

CHANGES IN SCHOOL-TO-WORK PARTICIPATION STW Elements Cohort I (1997) [1] % Weighted n Cohort II (1999) [2] % Weighted n Career Major/Pathway Tech Prep Coop Ed. (11 th & 12 th grade) Job Shadow Mentoring School Enterprise Apprenticeship/ Internship (11 th & 12 th grade) No STW programs 18.2 % 1,781,576 7.6 % 745,662 9.2 % 199,952 12.6 % 1,236,990 4.7 % 461,748 9.1 % 891,806 6.6 % 141,907 69.1 % 6,779,809 17.4 % 2,488,506 6.6 % 941,471 8.4 % 563,074 11.4 % 1,631,443 4.6 % 662,803 5.4 % 778,489 6.2 % 412,945 72.7 % 10,415,489 Total 9,815,752 14,324,266 [1] Analysis is based on 9 th to 12 th grade in Cohort I (n = 4469, Weighted n = 9,815,752) [2] Analysis is based on 9 th to 12 th grade in Cohort II (n = 6195, Weighted n = 14,324,266) Table III. Comparison of Cohort I and Cohort II: participation rate in school-towork elements in NLSY97 Parallel to these findings are reports by youth of school offerings of STW and Career Pathways. As shown in Table IV, the absolute number of students reporting that their schools offered STW or Career Pathways has risen but the proportion has declined over the course of the two waves of data collection with the exception of students enrolled in vocational schools. We then turned our attention to the interesting but perplexingly high percentage of 9th and 10th graders who reported participation in cooperative education. Bishop et al (2000) found that 7.3% of the first wave of grade 9th 12th respondents reported Cooperative Education participation though few were juniors or seniors, the age at which most such programs are available. Bishop concluded they may have confused Cooperative Education with cooperative learning during the interview. Delci & Stern (1999) reported approximately the same data but did not comment on the large number of 9th and 10th grade participants. 545

James R. Stone III Cohort I Schools Cohort II Schools School STW Career Pathway STW Career Pathway Weighted n % Weighted n % Weighted n % Weighted n % Public []1] 2,887,957 31.4 1,172,928 18.4 3,509,691 24.3 2,209,738 15.3 Vocational [2] 15,480 19.5 7,569 10.9 64,228 22.1 63,961 22.0 Private [3] 139,998 35.5 69,976 20.1 203,884 23.4 126,146 14.5 Others [4] 31,460 29.2 24,566 20.4 123,269 24.7 88,662 17.8 Total Weighted n of Students 9,780,807 16,092,218 [1] Students in public schools: Weighted n Cohort I = 9,199,183 / Cohort II =14.431,754 [2] Students in vocational schools: Weighted n Cohort I = 79321 / Cohort II = 290,713 [3] Students in private schools: Weighted n Cohort I = 394736 / Cohort II = 871,124 [4] Others: Weighted n Cohort I = 107,564 / Cohort II = 498,627 Table IV. Proportion of schools by type offering STW and career pathways: comparison Cohort I and Cohort II. We explored these curious findings further and found that 6.2% and 5.9% of 9th and 10th graders respectively reported participating in Cooperative Education. A similarly high percentage reported participating in Internships and Apprenticeships (Table V). While there are Cooperative Education programs that do serve some 9th graders such as Work Experience and Career Exploration Program (WECEP), these numbers appear to be too high. These numbers drop in the second cohort (Table VI) as they do for all grade levels. Grade Job Shadowing Mentoring Cooperative Education School Based Enterprise Tech Prep Apprenticeship /Internship 9 th 11.8 % 4.0 % 6.2 % 8.4 % 6.8 % 4.0 % 70.8 % 10 th 12.0 % 4.1 % 5.9 % 8.9 % 7.8 % 3.3 % 70.6 % 11 th 15.1 % 6.6 % 9.1 % 10.6 % 8.5 % 6.8 % 63.0 % 12 th 15.0 % 12.3 % 10.8 % 12.2 % 10.9 % 4.1 % 68.4 % [1] Weighted Proportions: Total Weighted n = 9,815,752 Table V. Students reports of STW Participation by grade: Cohort I. [1] No STW 546

CHANGES IN SCHOOL-TO-WORK PARTICIPATION Grade Job Shadowing Mentoring Cooperative Education School Based Enterprise Tech Prep Apprenticeship /Internship No STW 9 th 10.5 % 3.8 % 3.9 % 4.6 % 4.3 % 2.4 % 77.2 % 10 th 10.9 % 4.4 % 5.1 % 6.3 % 5.2 % 2.4 % 75.0 % 11 th 9.7 % 4.6 % 6.9 % 4.6 % 7.8 % 4.6 % 72.6 % 12 th 14.7 % 5.9 % 9.9 % 6.4 % 9.6 % 7.8 % 64.8 % [1] Weighted Proportions: Total Weighted n = 14,324,266 Table VI. Students reports of STW participation by grade: Cohort II. [1] To determine if these students may have been in Cooperative Education or any other work-based learning program, we examined the number of hours they reported working. While those 9th graders who reported participating in Cooperative Education also reported twice as many hours worked the previous year as did their grade level cohort, interpolating the annual data show they still averaged less than two hours a week in Cohort I and about 3 hours a week in Cohort II (Table VII). 10th grade students in both cohorts worked more but still less than five hours a week. One-and-a-half or five hours per week are too few hours worked for a traditional Cooperative Education programs. A further examination of these data show that a high proportion of 9th and 10th graders also report participating in Tech Prep (Table V and VI). All this suggests that analyses of STW participation using the NSLY97 self-report data should disaggregate the data by grade level for some analyses. For most of the following, we report only findings from 11th and 12th grade students when analyzing cooperative education or apprenticeship. General Characteristics of Youth Participating in the Four Curricular Concentrations There was a slight change in the percentage of students reporting participation in the four Curricular Concentrations between the two rounds of BLS data collection. As shown in Table VIII, there was a drop in the proportion of students reporting they were Academic Concentrators and a slight drop in those reporting as Dual Concentrators. On the other hand, there was an increase in the proportion of students reporting they were General or CTE Concentrators. 547

James R. Stone III [1] [2] Weighted n = 9,598,572 Weighted n = 14,156,861 Table VII. Report of hours worked in the previous year by grade level and enrollment status in cooperative education and apprenticeship/internship. 548

CHANGES IN SCHOOL-TO-WORK PARTICIPATION Cohort I Cohort II Concentration Description % Weighted n General Concentrator Academic Concentrator CTE Concentrator Dual Concentrator % Weighted n General program 55.3 5,353,449 58.5 7,397,628 College preparatory, academic or specialized academic program CTE, business and career program Combination academic and CTE program 34.1 3,304,446 29.9 3,777.562 4.9 476,288 6.1 775,491 5.6 546,749 5.5 692,584 Total 100 9,680,932 100 12,643,265 Table VIII. Participation in curriculum: General, Academic, CTE, and Dual Concentrations. Weighted Proportions: Cohort I: n = 4,399; Cohort II: n = 5,512. Part of the explanation for this may be found in the self-reports of coursetaking in math, science and vocational subjects (Table IX). While direct comparisons between the two rounds of BLS data collection are not possible due to changes in the questioning protocol, we can compare the percentage of students reporting no math courses taken which dropped from 19.9 to 5.1%. There was a similar drop in the percentage of students who reported taking no science courses (31.2% to 13.1%). These two findings are consistent with the sharp rise in the percentage of students who reported taking no vocational subjects (10.2% to 27.2%) (Table IX). Table X displays the demographic characteristics for all students in the first two rounds of the BLS survey. With this table we can observe changes in student characteristics in the two academic-year interval between the BLS data collections. Of particular interest in this study are changes in gender and geographic distribution of participants. We also explore the changes in curriculum concentration across the grade levels and changes in key demographics between Cohort I and Cohort II. 549

James R. Stone III Cohort I Variable n/ Weighted n % n/ Weighted n Cohort II % Math Basic Math Algebra I Geometry Algebra II Trigonometry Pre-calculus Calculus Other Advanced Math Other Math Class No Math [1] Science Biology Chemistry Physics Other Science Class No Science [1] Other Vocational Subjects Basic Computer Literacy Word Processing Computer Programming Other Computer Courses Shop/Industrial Arts Home Economics None [1] 2,188 4,775,957 2,188 4,776,305 2,190 4,779,457-79.0 36.4 21.4 5.3 3.4 0.7 - - 19.9 61.2 18.3 13.0-31.2 47.7 36.8 5,646 12,965,968 5,643 12,962,892 39.3 54.5 29.8 24.3 6.6 5.9 1.8 3.0 10.5 5.1 46.9 20.9 11.7 46.8 13.1 [1] Student had not taken and was not taking any of these courses Table IX. Participation in programs in Math, Science, Others Vocational Subjects weighted proportion. Both CTE and Dual Concentrators become more female and more white between the two academic years. The proportion of urban students reporting CTE Concentrator or Dual Concentrator status increased during this time as well. The geographic distribution of CTE and Dual Concentrators seems to have shifted as well. There are proportionately more CTE and Dual Concentrators from the West and less from the Northeast. 17.7 23.6 40.4 48.9 10.2 5,645 12,963,564 27.6 26.8 12.2 20.3 21.0 24.5 27.2 550

CHANGES IN SCHOOL-TO-WORK PARTICIPATION Table X. Percentage of students in each major curricular category: total samples. 551

James R. Stone III Table X continued. Percentage of students in each major curricular category: total samples. 552

CHANGES IN SCHOOL-TO-WORK PARTICIPATION Table X continued. Percentage of students in each major curricular category: total samples. Cohort I: n = 3,939, Weighted n = 9,680,929; Cohort II: n = 5,158, Weighted n = 12,643,263. 553

James R. Stone III Delci & Stern (1999) noted that students begin to identify with a curriculum concentration as they move from 9th to 11th grade. As we look at Cohort II in Table X, we find only a slight decrease in the General Concentrators between 9th and 11th grade. Also for Cohort II, we find a decrease in the percentage of students identifying as an Academic Concentrator in grades 10 through 12 however. This is accompanied by a decrease in CTE Concentrators in Grades 10 and 11 with a small increase in grade 12. The proportion of Dual Concentrators decreased in Grades 10 through 11 and increased in grade 12. A further examination of Table X shows that between the two academic years, CTE and Dual Concentrators became more urbanized and poorer. Compared to Cohort I, fully 38.4% of CTE Concentrators and 43.5% of Dual Concentrators in Cohort II are urban. Both curriculum concentrations saw an increase in the proportion of students coming from low income households. Curiously, the proportion of CTE and Dual Concentrators whose fathers have had a least some college education increased. More CTE and Dual Concentrators come from Vocational High Schools in Cohort II while the proportion of CTE Concentrators in Public High schools declined. More CTE and Dual Concentrators came from schools with 1000 students or over in Cohort II. The Relationship between STW and CTE Participation and Curricular Concentration We created a series of logistic regression models to examine the factors that predict those students who report themselves as Academic concentrators, CTE Concentrators, Dual Concentrators or General Concentrators. The models presented here are designed to control for students background characteristics including gender, race, socioeconomic status, 8th grade academic performance, enrollment status, urbanicity and region of the country. The key variable of interest in these models is student self-report of participation in STW. In both Cohorts, we did not find any effect of gender or urbanicity in predicting Curriculum Concentration. There are other socioeconomic status (SES) effects but they are not consistent. Measures of SES were found to be significant predictors of General and Academic concentrations for both Cohorts. There were regional effects in Cohort I but not in Cohort II (Table XI and XII). 554

CHANGES IN SCHOOL-TO-WORK PARTICIPATION Dependent Variable: Curricular Concentration Independent General Academic CTE Dual Variable B Exp (B) B Exp (B) B Exp (B) B Gender.120 1.128 -.186.830.040 1.041.239 Exp (B) 1.270 Black Hispanic -.151.295 *.860 1.342.052 -.301 1.054.740.311.214 1.365 1.239.209 -.353 1.232.703 HH Income Father s Ed. Mother s Ed. -.380 *** -.049 -.012.684.953.988.574 ***.060.044 1.776 1.062 1.045 -.422 * -.215 *.004.655.807 1.004 -.245.099 -.142.783 1.104.868 Urban Suburban.114.195 1.121 1.215 -.199 -.122.820.885.227 -.245 1.255.783.089 -.035 1.093.965 North Central South West.850 ***.187.914 *** 2.339 1.205 2.494 -.677 ***.102 -.530 ***.508 1.107.589-1.666 *** -1.255 *** -1.933 ***.189.285.145.373.173 -.202 1.452 1.188.817 STW / No CP STW / CP No STW/ CP Enrollment 8 th Grade GPA n -2 Log likelihood -.632 *** -.611 *** -.194 -.355 -.625 *** 2,331 2842.318.532.543.824.701.535 * = p <.05, ** = p <.01, *** = p <.001.265 * -.099 -.233.328.881 *** 2,331 2549.550 1.304.906.792 1.388 2.413 1.289 *** 1.788 *** 1.028 ** 1.138 -.526 *** 2,331 709.383 3.629 5.977 2.794 3.120.591.842 *** 1.197 ***.949 ** -.257 -.114 2,331 46.848 Table XI. Logistic regressions predicting curricular concentration for Cohort I. 2.322 3.309 2.582 Looking through the lens of STW participation, we find that Cohort I students who participated in STW only, compared to those who reported No STW and No Career Pathways (the omitted variable) were less likely to classify themselves as General Concentrators and more likely to report themselves as Academic, CTE or Dual Concentrators. There is a similar but slightly different pattern for those who reported participation in STW and Career Pathways. These students, compared to those who report No STW and No Career Pathways, were less likely to be in a General Concentration and more likely to be in a CTE or Dual Concentration. Those who participated in Career Pathways only, compared to those reporting No STW and No Career Pathways, were more likely to report participation in a CTE or Dual Concentration..773.893 555

James R. Stone III Dependent Variable: Curricular Concentration Independent Variable Gender General Academic CTE Dual B Exp B Exp B Exp B (B) (B) (B) -.048.935.096 1.101.103 1.109 -.250 Exp (B).779 Black Hispanic.003.100 1.003 1.105 026 -.007 1.027.993 -.082 -.370.921.691 -.022 -.052.978.949 HH Income Father s Ed. Mother s Ed. -.081 -.087 **.157 ***.922.916 1.170.049.101 *** -.140 *** 1.050 1.106.870 -.057 -.069.046.944.934 1.047.237.069 -.224 ** 1.267 1.071.799 Urban Suburban.126 -.034 1.134.966 -.063.198.939 1.219 -.077 -.311.926.733 -.216 -.278.806.757 North Central South West -.064 -.096.012.938.908 1.012.184.098 -.054 1.202 1.102.947 -.494 -.129 -.004.610.879.996.074.218.167 1.077 1.243 1.182 STW / No CP STW / CP No STW/ CP Enrollment 8 th Grade GPA n -2 Log likelihood -.005.083 -.046 -.550 *** -.093 * 3,176 4,240.382.995 1.087.955.577.911 -.049 -.053.048 1.050 ***.143 ** 3,176 3,761.484 952.948 1.050 2.856 1.154 * = p <.05, ** = p <.01, *** = p <.001 CTE Model is not significant at the level of α =.05. -.334.245 -.257 -.624 ** -.063 3,176 1,369.142.716 1.277.773.536.939 Table XII. Logistic regression predicting curricular concentration for Cohort II..439 * -.634.257 -.142 -.058 3,176 1,354.079 1.551.530 1.293 We did not find the same patterns for Cohort II. Compared to those who reported No STW and No Career Pathways, Dual Concentrators were more likely to report participating in STW only (Table XII). However, Enrollment status was a significant predictor of Curricular Concentration. Students not enrolled in school at the time of the second data collection were more likely to self-identify as a General Concentrator and CTE Concentrator and less likely to self-identify as an Academic Concentrator. Controlling for background characteristics, the relationship between Curricular Concentration and STW participation for Cohort I can be.867.944 556

CHANGES IN SCHOOL-TO-WORK PARTICIPATION summarized as follows, compared to students who report No STW participation and no participation in Career Pathways: Students who report STW participation and no participation in Career Pathways are more likely to be Academic, CTE or Dual Concentrators and less likely to be General Concentrators. Students who report STW and Career Pathway participation are more likely to be Dual or CTE Concentrators and less likely to be General Concentrators. Students who report no STW participation but did participate in Career Pathways are more likely to be Dual or CTE Concentrators. The data for Cohort II is less conclusive showing essentially no Curricular Concentration and STW participation relationship. Predicting Participation in STW Programs and Elements From the more general examination of curricular concentrations and STW participation, we now look more closely at the factors that predict participation in specific elements of STW. We parse this analysis in three ways across the two cohorts. First, we analyze the simple question of participation in STW or Career Pathway. Then we investigate the four combinations of STW and Career Pathway configurations identified earlier. Finally we look at predictors of participation in these specific STW activities: tech prep, cooperative education, job shadowing, mentoring, school-based enterprise and apprenticeship/internship. Each model employed the same analytic model, and we used a series of dummy coded variables. These included (omitted category in parentheses): Gender (female) Race (White) Ethnicity (non-hispanic) Urbanicity (rural) Location (northeast U.S.) Curricular concentration (Academic) In addition we included a number of continuous variables: Household income Parent s education 8th grade GPA For these logistic regression analyses, we have included the number of math, science and CTE courses in our prediction models. Delci & Stern 557

James R. Stone III (1999) found there were differences in the amount of algebra, geometry and algebra II course work taken as a function of curriculum concentration. We test the hypothesis that math, science and vocational course-taking predicts which of the curricular concentrations a respondent identifies with when important background characteristics are accommodated in the analysis. We have also included a measure of student enrollment at the time of the interviews (Cohort II). This variable, Enrollment, allows us to partial out the effect of students who have dropped out or stopped out of school. The dependent variables are selfreports of participation in one of the twelve prediction models explored in these data (Tables XIII-XVI) for cohorts I and II. Independent Variable Dependent Variable: STW Participation Any STW Career Pathway B exp(b) B exp(b) Gender -.110.896.122 1.130 Black Hispanic.272 -.381 1.312.683.567 ** -.079 1.763.924 HH Income Father s Ed. Mother s Ed. Urban Suburban North Central South West General CTE Dual Enrollment 8 th Grade GPA # of Math # of Science # of Vocation n -2 Log likelihood.069 -.076.007.029 -.108.413.405.522 * -.355 * 1.294 ***.646 *.504 -.018 -.042.132 -.003 1,135 1,361.514 1.072.927 1.007 1.029.897 1.511 1.500 1.685.701 3.647 1.908 1.655.982.959 1.141.997 -.080 -.004 -.089.259.030 -.220 -.197 -.237.128 1.426 *** 1.311 ***.270.282 * -.033.112.024 1,133 1,014.103.923.996.915 1.295 1.031.802.821.789 1.137 4.164 3.712 1.310 1.326.967 1.118 1.024 * = p <.05, ** = p <.01, *** = p <.001 Table XIII. Logistic regressions predicting STW participation for Cohort I. 558

CHANGES IN SCHOOL-TO-WORK PARTICIPATION Dependent Variable: STW Participation Independent Variable Any Career STW Pathway B exp(b) B exp(b) Gender -.056.946.050 1.051 Black Hispanic.358 *** -.292 * 1.430.746.282 * -.150 1.326.861 HH Income Father s Ed. Mother s Ed..023.029 -.018 1.023 1.030.892 -.178 *.009 -.038.837 1.009.962 Urban Suburban -.105 -.082.900.921 -.315 * -.238.729.788 North Central South West -.034 -.153.195.966.858 1.216 -.145 -.277 -.132.865.758.876 General CTE Dual Enrollment 8 th Grade GPA # of Math # of Science # of Vocation N -2 Log likelihood -.002 -.055.101.043 -.096 -.088 -.077.019.998.947 1.106 1.044 1.101.916.926 1.019 3,169 3,523.353 -.006.119 -.349.059 -.151 *.012 -.178 * -.010 2,821 2,585.509.994 1.127.705 1.061.860 1.012.837.990 * = p <.05, ** = p <.01, *** = p <.001 Table XIV. Logistic regressions predicting STW participation for Cohort II. We looked at factors that predict participation in STW and Career Pathways. We found that knowing the number of math, science and vocational courses taken does not help predict participation in STW or Career Pathways (Table XIII). Respondents who report participating in any STW are more likely to be from the West than from the Northeast United States. They are less likely to be General Concentrators and more likely to be CTE or Dual Concentrators than Academic Concentrators. Career Pathway participants are more likely to be Black than White, more likely to be a CTE or Dual Concentrator than Academic Concentrator. They also report higher GPA. 559