As we work on building the noncredit data infrastructure, we are often asked, “Why?” Why do we care about developing and improving noncredit data collection in states and institutions? It requires a lot of work and investment. For those who are engaged, the drive is clear; yet others not yet engaged rightfully ask whether it will be worth the effort.
Clearly, data in and of itself is not the essential goal, but rather it is what the data tell us and what noncredit stakeholders can do based on those data. Students, employers, policymakers, and institutions all benefit from noncredit data, but they use these data in different ways. So, it is important to reflect and consider the goals of data collection. Following are a few overarching reasons why these data are important:
Capturing the full mission. With noncredit education comprising nearly 40% of community college headcount enrollment nationally, robust noncredit data allows institutions and systems to communicate their full contributions toward preparing individuals and local economies for work. Noncredit students often are undercounted, which can result in noncredit programming—a major activity, particularly for community colleges—being overlooked and distorting measures of funding and activity.
Advocacy. As state entities and institutions seek to capture their full mission, as reflected in their program offerings, some use noncredit data to demonstrate value to their legislatures and other stakeholders when seeking funding and support. States and institutions are limited by the current data infrastructure and can’t demonstrate the full value of their program offerings when noncredit programs play an important part, but the related data are not adequately collected.
Quality assurance and accountability. Conversations about data are often tied to the concept of quality—which can mean different things depending on the goals and interests of various stakeholders. Knowing which programs are valuable for students is a pressing goal for many states and institutions. Credentials-of-value lists and program eligibility requirements based on noncredit data can be essential in guiding funding for noncredit programs. Workforce Pell is a strong example of how data can be used to this end, with the legislation’s current guardrails focused on completion rates, employment rates, and return on investment.
Program improvement and pathways development. To advance quality assessment efforts, states and institutions may seek to understand which programs lead to valuable outcomes, including further education and employment. This hinges on analyzing outcomes data for students who participate in noncredit offerings, as well as understanding the program characteristics associated with those outcomes. Based on this information, states and institutions may change the mix of programs offered or adapt the content of these programs. To ensure that students enrolled in noncredit programs have opportunities for further education, noncredit data are essential to building program pathways and tracking students throughout their educational journeys.
Learner navigation. When considering noncredit enrollment, potential students need to understand the nature of the program and what outcomes they can expect upon completion. Will they get the job they are hoping for? Will they be prepared to pursue the education they are seeking? Inconsistencies in the availability of noncredit data and associated information about educational and labor market outcomesmake comparisons across programs and institutions difficult. These inconsistencies make it challenging to assess the value and quality of short-term training, particularly for learners seeking to make decisions about how to invest their time and money.
Improving and expanding outcomes measures. Including noncredit data in state longitudinal data systems offers many possibilities for better understanding outcomes. At minimum, these data can provide an understanding of the basic outcomes of noncredit education, including completion and labor market outcomes. But expanded data systems can build on these measures to allow for the examination of a broader range of outcomes on wider populations of learners by linking with other public service systems, such as child welfare, health and human services, and corrections. Additionally, noncredit data can be built upon to include skills and competencies associated with credentials. These data can provide invaluable information to a range of stakeholders—particularly to employees who use credentials in hiring. By making these skills and competencies transparent, the nondegree credentials associated with noncredit education may become better understood and therefore more valuable.
Importance of collecting meaningful noncredit data. Collecting noncredit data is a critical step toward understanding and demonstrating the value of nondegree and workforce education programs. However, the usefulness of these data depends on their quality, accuracy, and consistency. Institutions must ensure that they collect data elements that are meaningful and aligned with reporting, planning, and decision-making needs, and that these data are captured correctly. High-quality data provide the foundation for effective analysis, workforce alignment, performance measurement, and the ability to tell the story of noncredit education. For example, accurately capturing CIP (Classification of Instructional Programs) codes for noncredit courses is essential. These codes support program classification, labor market analysis, credential reporting, and connections between noncredit and credit pathways.
Without reliable and complete data, institutions risk limiting their ability to measure outcomes, demonstrate impact, and inform strategic decisions.
The State Noncredit Data Project is the collective effort of a dozen researchers and is led by Michelle Van Noy, director of the Education and Employment Research Center at Rutger’s University, and Mark D’Amico, professor of higher education at the University of North Carolina at Charlotte.