In order for data to be trusted, policies and protocols must be in place to ensure consistent collection of reliable, valid and complete career readiness data. States can establish universal definitions and automated processes to collect and interpret data and work with practitioners and the public to foster an understanding of data elements to build trust in their data.

One example is Texas’ automated learner identification system and statewide programs of study. Prior to the establishment of this new system in 2015, each local school district could develop its own programs of study and course sequences. This led to inconsistent data collection, as districts could have different course requirements for the same program of study. Additionally, the state relied on districts to self-report the number of Career Technical Education (CTE) concentrators based on their locally-developed programs of study. Because districts decided which courses counted towards concentration or completion of a program of study, this meant that learners could complete a course in one district that counted towards one program of study, but that same course would not count in another district. 

Texas addressed this massive data challenge by creating new statewide programs of study and a uniform framework for collecting data. To develop these programs of study, several state agencies including the Texas Education Agency, Texas Workforce Commission, Texas Workforce Investment Council, and the Texas Higher Education Coordinating Board worked together to leverage labor market information and set benchmarks for in-demand, high-wage, and high-skill occupations. Related occupations that satisfy the criteria were then grouped together and a set of course sequences with accompanying course codes were developed to form a program of study. Because of these changes, every school district now has the same grouping of available aligned courses for each program of study. 

Read the Advance CTE Case Study Collecting Data Consistently and Accurately: Texas’ Automated Learner Identification System and Statewide Programs of Study to learn more about how Texas is improving CTE data quality. For additional resources on improving the quality and use of career readiness data, check out the Career Readiness Data Quality microsite  

This is the first edition in a series of Advance CTE data quality blogs to accompany Advance CTE’s latest releases, Career Readiness Data Quality and Use Policy Benchmark Tool and Data Quality Case Studies. For more resources on data and accountability in CTE, please visit the Learning that Works Resource Center.

Brian Robinson

Policy Associate