In our 2019 landscape report, we identified stakeholders from the learning ecosystem that serves the frontline workforce, including employers, government agencies, adult education and workforce providers, and higher education institutions.
As part of this work, we explored how these institutions used worker data to streamline services, improve training, and advance opportunities for workers in sectors such as healthcare, retail, restaurants, hospitality, and manufacturing. We learned that the ecosystem is complex and siloed, and it removes agency from the worker by taking data without returning any value. Further, although many of the same data types are collected across agencies and stakeholders, much of it is duplicative and data are rarely used to inform program decisions or to understand collective impact and long-term outcomes.
As a result, we identified six key recommendations to promote a more data-driven, collaborative, and frontline worker-centered learning ecosystem:
Our most recent efforts focused on the first recommendation: increase collective demand for data-sharing practices that drive opportunities for frontline workers.
Our goal was to examine practices that work and secure stakeholder commitment to systems and policies that enable data interoperability in the frontline learning ecosystem.To meet this goal, we designed a multifaceted research project that addressed the following research questions:
Digital Promise designed a qualitative research framework driven by inclusivity and equity to conduct data collection, thematic analysis, and collaborative interpretation aligned with our research questions. We developed original interview and case study protocols tailored to each of the major stakeholder groups previously identified in our 2019 findings (see: participants). Data collection and analysis occurred concurrently from August 2019 through January 2020.
(Download Executive Summary to read more.)
To select interview and case study participants for the project, we employed Maxwell’s (2005)4 method of purposeful selection, a strategy in which stakeholders are selected deliberately in order to provide information that cannot be collected by random sampling methods. The research team chose to implement the purposeful selection strategy to ensure participants represent diverse experiences and perspectives across the adult learning ecosystem. Researchers first defined the dimensions of variation in the stakeholder groups that were most relevant to data interoperability as it relates to frontline worker advancement (see Table X). For the purpose of this study, dimensions included: demographic and background data, industry, geographic area, stakeholder needs, resource availability, relationship to Digital Promise, and knowledge related to data interoperability in the adult learning sphere serving frontline workers. It is important to note that some participants were also selected through convenience sampling, or sampling through existing connections, given Digital Promise’s existing network and relationships in the field.
(Download Executive Summary to read more.)
We conducted a three-part thematic analysis of the qualitative data. First, we pulled coded excerpts for each stakeholder group from Dedoose according to the following categories: incentives, challenges, learnings from the field, technology, drivers of change, and readiness to spur demand. During this process, we analyzed more than 2,000 excerpts and identified descriptive quotes to support our analysis. Next, we developed stakeholder-level memos to synthesize the findings by stakeholder and theme. The four categories that emerged most often included: barriers, benefits, drivers of change, and readiness to spur demand. Finally, we conducted a cross-stakeholder analysis to highlight shared incentives and challenges across stakeholder groups in order to devise a strategic plan.
(Download Executive Summary to read more.)
Adult Learning