USE DATA TO DRIVE
EQUITY AND QUALITY
CHAPTER 8
8
COLLECTING DATA ON work-based learning
experiences is key to spotting trends,
highlighting promising practices, and identifying
and addressing gaps. The vast majority of
states collect and disaggregate data on CTE
programming to meet the data reporting
expectations outlined in Perkins V. These data
often include work-based learning opportunities,
especially in states where work-based learning
courses have unique course codes that
enable states to collect participation data and
disaggregate it by student demographics.
However, many states oer work-based learning
opportunities outside of CTE programming that
are not measured in this CTE data reporting,
meaning that CTE-based work-based learning
data tell just a portion of the story.
Just 20 states collect comprehensive data
on work-based learning participation, including
student outcomes. Virginia, for example, oers
11 types of work-based learning experiences
and collects data on all types, including
demographics and special populations, three
times a year through its Master Schedule
Collection and Student Record Collections
processes. Tennessee has created the WBL
Student Placement Portal, in which all work-
based learning programs are required to share
information about work-based learning student
participation and outcomes.
States need to be able to disaggregate
data to ensure high-quality work-based
learning opportunities are accessed at similar
rates across dierent student groups, with no
equity gaps. Most states can disaggregate
CTE program data, which frequently includes
work-based learning program data. Fewer
have comprehensive data systems that allow
them to both capture and disaggregate data
for all work-based learning programming, in
and out of CTE courses. To identify trends in
work-based learning participation, just 11 states
can disaggregate that comprehensive data by
gender, ethnicity, income, geography, and type
of experience (e.g., industry sector, internship
vs. apprenticeship, etc.) at a minimum. For
example, Virginia’s Master Schedule Collection
referenced above captures work-based
learning participation by gender, ethnicity,
English learners, students with disabilities,
economically disadvantaged, military connected,
unaccompanied homeless, youth in foster care,
single parents, nontraditional students, and
out of workforce. School districts can access
subgroup-level student data for each work-
based learning experience.
As part of its annual CTE report, Iowa
disaggregates work-based learning participation
data over time by school size and service area,
student grade level and gender. The report
also makes comparisons across the same
categories for work-based learning participation
and general CTE participation. Maryland
collects work-based learning data through its
accountability system and disaggregates those
data by student demographics. In addition,
Maryland collects some student-level data
through a work-based learning survey system
and disaggregates the data by gender, race,
and experience type, among other factors. The
BEST PRACTICE:
HIGH SCHOOL WORK-BASED LEARNING: A BEST PRACTICES GUIDE 26