In this report, Data Quality Campaign (DQC) and the Workforce Data Quality Campaign (WDQC) gathered recommendations from education, research, workforce, and policy experts to outline how these entities can collaborate to create state policy that links K-12 and workforce data systems. Connecting high-quality data between the two systems “can help both agencies analyze workforce needs; understand the quality and benefits of work-based learning opportunities and job placements; and calculate the return on investment for programs such as CTE, career academy programs, and apprenticeships.”
To develop diverse and reliable career pathways for students, education and workforce agencies must collaborate to decide how they will review and use data they collect and link. “Setting mutual goals to communicate critical data points demonstrates an understanding that students take a different pathway during and after their K-12 education and that their lives are interconnected across several systems.” Additionally, agencies should follow their state’s Workforce Innovation and Opportunity Act and Perkins Act plans to align definitions of reporting standards and secure data linkages across programs.
For example, in Kentucky, the Kentucky Center for Education and Workforce Statistics (KCEWS) a collaboration between state K-12, postsecondary and workforce development agencies “collects and links education and workforce data to provide public reports to empower policymakers and the people of Kentucky to make informed decisions.” A collaborative staff meets with workforce planning regions and local workforce areas across the state to inquire about their data needs. From this information, KCEWS provides the Kentucky Future Skills Report, “an interactive tool that allows employers and practitioners to view historic workforce supply data by industry and credential type and compare it to future workforce demand.”
Cross-Agency Data Governance
Quality cross-agency data governance defines the roles/responsibilities of agencies in the collection of data and helps to identify questions and answers about K-12 and workforce data linkages. For example, asking whether students are earning certifications and getting employed in local high-demand jobs can, in general, only be answered through cross-agency data governance. For policymakers, DQC offers recommendations on implementing a high-quality data governance structure in their Roadmap for Cross-Agency Governance.
Data Matching and Sharing
“A high quality data matching process uses individually identifying information (e.g., Social Security number, name, date of birth) to ensure that the records for the same individual are being accurately matched between data systems.” Secure, cohesive data matching and sharing provides “transparency and helps answer questions about program outcomes, resource allocations, nontraditional pathways through education and the workforce, the value of different credentials, gaps between the education and skills of an individual and workforce needs, equitable access to work-based learning, and more.”
The non-profit Credential Engine, offers their Credential Registry and Credential Finder tools for employers and educators to list their available credentials “and to search, discover, and compare credentials and competencies they are interested in.” Also, students and workers can use the tools to research the credential market and to find education and career pathways that best suit their needs. These tools provide a “common language around credentials, competencies, quality insurance, pathways, employer preferences, and labor market outcomes” for both jobseekers and employers, improving postsecondary and labor market transparency.
Data Analysis and Use
Analysis of student data is critical for identifying needs of specific populations and making informed decisions. Teachers, program administrators, education and business leader, employers, researchers, and other partners need data analysis to properly track student progress and evaluate outcomes of the education and workforce systems at large. For example, in New Jersey, the statewide longitudinal data system, the New Jersey Education to Earnings Data System (NJEEDS), stores high school, college, and career data. Through partnerships with Rutgers University’s John J. Heldrich Center of Workforce Development and state agencies, NJEEDS supported the state’s Office of Career Readiness (OCR) in identifying “not only 68 high-paying jobs in in-demand industries but also a looming teacher shortage.” Through these findings, OCR was able to address future CTE teacher shortages in the STEM fields by developing policy that identified career clusters employing high (close to retirement) median-age teachers and increase recruitment efforts in those areas.
For K-12 and workforce data linkages to be sustainable, states need to make long-term investments to ensure linkages continue to meet people’s needs during changes in leadership or transitions. To strengthen data systems’ reliability and usability, “agencies need to think creatively about the outputs of the linked K-12 and workforce data, such as data tools, apps, or code.” Specifically, through sharing agencies’ coded system data, states can create an effective ecosystem “of public-facing tools so that civic data entrepreneurs and data customer, such as students themselves, can access and use data in the social media space.” In addition, states need to appropriate annual funding to agencies, programs, schools, and districts so they have the capacity to understand and use their own data for making informed program and policy decisions.
Washington’s Education Research & Data Center (ERDC) complies longitudinal data of students as they move through school to the workforce and has built the state’s capacity to link, share, and use workforce data. To begin organizing data for dissemination, ERDC staff developed a list of research questions about education and employment they wanted answered. For instance, ERDC grouped research questions into two categories: employment status of students while enrolled and employment as an outcome post high school (including and their relationship to each other). ERDC then used existing data quality strategies to determine who would use the data and how and when to share the data in an understandable way. ERDC research such as, Workforce Participation, Washington State High School Graduates, 2008–09 emanates from K-12 and workforce data linkages, and its methodologies are documented in ERDC’s Employment Data Handbook, giving context to the data and thereby improving its usability.
Privacy and Security
“Methods to ensure data privacy and security must be in place, particularly when dealing with potentially sensitive information about students.” The following privacy protection laws and policies are necessary for a system that supports trustworthy data linkages:
- The Family Educational Rights and Privacy Act (FERPA) protects the privacy of student education records. “The law applies to all schools that receive funds under an applicable program of the U.S. Department of Education.”
- Management and staff working with data “must be aware of or had training on all federal, state, and local privacy legislation and on the interplay among these laws. They are also aware of how state laws affect the sharing of information and specific exceptions governing research studies.”
- “A data inventory or data classification defines each data element collected and stored by the state. The inventory or classification is regularly reviewed and updated. Levels of data sensitivity are clearly defined, and data is categorized by these levels, with corresponding differences in levels of protection depending on the sensitive nature of data.”
- “A trusted repository of data that can be shared but does not compromise privacy must be in place.”
For state policymakers, more information on developing privacy policies and procedures can be found at the US Department of Education Privacy Technical Assistance Center’s website, the Family Policy Compliance Office website, and DQC’s Roadmap to Safeguarding Student Data.
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