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What Can We Learn About Career
Readiness Interventions from
Large-Scale, Longitudinal Datasets?
OECD Disrupted Futures Conference
June 1, 2023
Thomas Torre Gibney, M.P.P., Senior Research Associate
Literacy Program at WestEd
Center for Economic Mobility at WestEd (USA)
Cameron Sublett, Ph.D., Associate Professor and Director
Educational Leadership & Policy Studies
University of Tennessee, Knoxville (USA)
Who We Are
By the end of today’s session, we hope you’ll have gained
new knowledge about…
• The modern-day policy context around college and career
readiness in the United States
• The promise and increasing availability of large-scale,
longitudinal datasets and their role in informing policy
• A small slice of the research evidence emerging from the
use of these datasets
The policy context: Putting the “and” back into college and career
readiness in the United States
https://www.edweek.org/teaching-learning/how-career-prep-programs-went-from-dumping-ground-to-top-priority/2023/04
Historically:
• Stratified system of K–12 and postsecondary/tertiary education, which has served to sort
students into different tracks based on perceived abilities and other characteristics
• Racial minorities, students of lower socioeconomic status, and low-achieving students more
likely to be sorted into tracks leading to jobs with little promise of economic mobility
• “Career readiness” programming in U.S. has been traditionally relegated to these so-called
vocational tracks
The policy context
Now:
• Greater interest in and funding for career readiness programming
• Increased attention to issues of equity throughout the education system
• Rise of large-scale, longitudinal datasets (3 prominent types):
• Nationally representative surveys from federal agencies like NCES
• Statewide longitudinal data systems (SLDSs) that connect data from different state agencies
• Collaborative data structures built and managed by non-profit/non-governmental organizations (e.g.,
National Student Clearinghouse)
The policy context
Features of these datasets that make them especially promising now:
• Ability to track individuals over time and across sectors/segments of P-20-W systems
• Many observations allow for greater computing power and ability to detect the effects of
different interventions
• Data often linked to rich demographic, geographic, and programmatic records allow
researchers to test hypotheses related to inequalities in the provision of career readiness
What do these trends mean for research and policy?
As a result, these datasets allow us to answer pressing, policy-relevant questions, such as:
• What are the effects of an intervention on students’ graduation, achievement, and labor
market outcomes?
• Is there evidence of equitable access to the intervention across racial/ethnic groups, family
income levels, gender, English learner status, and geographic locale?
• Under what conditions do program participants receive the greatest benefits? What factors
inhibit or facilitate successful program provision?
What do these trends mean for research and policy?
• Dougherty (2018) on the effect of whole-school CTE programs on high school graduation
• Edmunds et al. (2022) on postsecondary transitions for students who participate in statewide
CTE dual enrollment pathways
• Torre Gibney & Rauner (2021) on the role of a supportive adult in relation to students’ college-
going behaviors, when part of an intentional education and career planning process
• Sublett, Plasman, & Lara-Brady (forthcoming) on gender stratification within STEM-oriented
career education pipelines
What is the field learning from these developments?
Students who received support from an adult to develop an ECP in grade 9 were more likely than those who did
not to complete a college preparatory curriculum, enroll in college, and enroll in a bachelor’s degree program.
Values in average probabilities; * p < .05; ** p < .01; *** p < .001
Students who reviewed their plan yearly with an adult in school were more likely than those who did not to apply
for federal financial aid to assist with college costs, apply to college, and enroll in college
Values in average probabilities; * p < .05; ** p < .01; *** p < .001
Gender stratification within STEM-oriented career education pipelines is a complex story.
Gender stratification within STEM-oriented career education pipelines is a complex story.
Gender stratification within STEM-oriented career education pipelines is a complex story.
How are linked data systems being used to answer policy-relevant
questions related to career readiness in your countries?
How can access to and usage of these systems be improved?
Q&A
Thank you!
Connect with us!
Thomas Torre Gibney
ttorreg@wested.org
https://www.linkedin.com/in/thomastorregibney
Cameron Sublett
csublet1@utk.edu
Resources
https://www.wested.org/area_of_work/literacy/
https://economic-mobility.wested.org/
https://statedata.wested.org/
https://elps.utk.edu/
Visit WestEd.org to Learn More and Connect with Us

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Disrupted Futures 2023 | Learning from large-scale, longitudinal datasets

  • 1. What Can We Learn About Career Readiness Interventions from Large-Scale, Longitudinal Datasets? OECD Disrupted Futures Conference June 1, 2023
  • 2. Thomas Torre Gibney, M.P.P., Senior Research Associate Literacy Program at WestEd Center for Economic Mobility at WestEd (USA) Cameron Sublett, Ph.D., Associate Professor and Director Educational Leadership & Policy Studies University of Tennessee, Knoxville (USA) Who We Are
  • 3. By the end of today’s session, we hope you’ll have gained new knowledge about… • The modern-day policy context around college and career readiness in the United States • The promise and increasing availability of large-scale, longitudinal datasets and their role in informing policy • A small slice of the research evidence emerging from the use of these datasets
  • 4. The policy context: Putting the “and” back into college and career readiness in the United States https://www.edweek.org/teaching-learning/how-career-prep-programs-went-from-dumping-ground-to-top-priority/2023/04
  • 5. Historically: • Stratified system of K–12 and postsecondary/tertiary education, which has served to sort students into different tracks based on perceived abilities and other characteristics • Racial minorities, students of lower socioeconomic status, and low-achieving students more likely to be sorted into tracks leading to jobs with little promise of economic mobility • “Career readiness” programming in U.S. has been traditionally relegated to these so-called vocational tracks The policy context
  • 6. Now: • Greater interest in and funding for career readiness programming • Increased attention to issues of equity throughout the education system • Rise of large-scale, longitudinal datasets (3 prominent types): • Nationally representative surveys from federal agencies like NCES • Statewide longitudinal data systems (SLDSs) that connect data from different state agencies • Collaborative data structures built and managed by non-profit/non-governmental organizations (e.g., National Student Clearinghouse) The policy context
  • 7. Features of these datasets that make them especially promising now: • Ability to track individuals over time and across sectors/segments of P-20-W systems • Many observations allow for greater computing power and ability to detect the effects of different interventions • Data often linked to rich demographic, geographic, and programmatic records allow researchers to test hypotheses related to inequalities in the provision of career readiness What do these trends mean for research and policy?
  • 8. As a result, these datasets allow us to answer pressing, policy-relevant questions, such as: • What are the effects of an intervention on students’ graduation, achievement, and labor market outcomes? • Is there evidence of equitable access to the intervention across racial/ethnic groups, family income levels, gender, English learner status, and geographic locale? • Under what conditions do program participants receive the greatest benefits? What factors inhibit or facilitate successful program provision? What do these trends mean for research and policy?
  • 9. • Dougherty (2018) on the effect of whole-school CTE programs on high school graduation • Edmunds et al. (2022) on postsecondary transitions for students who participate in statewide CTE dual enrollment pathways • Torre Gibney & Rauner (2021) on the role of a supportive adult in relation to students’ college- going behaviors, when part of an intentional education and career planning process • Sublett, Plasman, & Lara-Brady (forthcoming) on gender stratification within STEM-oriented career education pipelines What is the field learning from these developments?
  • 10. Students who received support from an adult to develop an ECP in grade 9 were more likely than those who did not to complete a college preparatory curriculum, enroll in college, and enroll in a bachelor’s degree program. Values in average probabilities; * p < .05; ** p < .01; *** p < .001
  • 11. Students who reviewed their plan yearly with an adult in school were more likely than those who did not to apply for federal financial aid to assist with college costs, apply to college, and enroll in college Values in average probabilities; * p < .05; ** p < .01; *** p < .001
  • 12. Gender stratification within STEM-oriented career education pipelines is a complex story.
  • 13. Gender stratification within STEM-oriented career education pipelines is a complex story.
  • 14. Gender stratification within STEM-oriented career education pipelines is a complex story.
  • 15. How are linked data systems being used to answer policy-relevant questions related to career readiness in your countries? How can access to and usage of these systems be improved? Q&A
  • 16. Thank you! Connect with us! Thomas Torre Gibney ttorreg@wested.org https://www.linkedin.com/in/thomastorregibney Cameron Sublett csublet1@utk.edu
  • 18. Visit WestEd.org to Learn More and Connect with Us