Designing Recognition that Works: Connecting the Dots from Experience to Credential with MyCPL

Abstract

This presentation explored how recognition systems can be designed to work more effectively by connecting lived experience to credentials through trust-centered, data-informed practices, illustrated by the development of MyCPL (Credit for Prior Learning). Framed against a period of rapid disruption likened to a societal “tornado” as in the Wizard of Oz, the session argued that attempts to overly control complexity in learning, workforce, and credentialing systems are ultimately unsustainable. Instead, recognition should be approached as part of a decentralized learning ecology grounded in human intelligence, trust, and shared responsibility. 

Drawing on two decades of work in prior learning assessment and recognition (PLA/PLAR), data standards, and large-scale system integration, David Moldoff traced the evolution from reactive, costly recognition processes toward more proactive models. These proactive approaches include structured transfer pathways, credential mapping, and learner-centered workflows that reduce friction while increasing transparency and fairness. Central to this shift is the use of structured data standards (e.g., schema-based representations such as Credential Transparency Description Language or CTDL) that enable interoperability, reduce duplication, and improve visibility in search and AI-driven environments. As search engines increasingly prioritize structured data over free-text content, institutions that fail to adapt risk diminished discoverability and competitiveness for learners and employers alike. 

The presentation emphasized trust—described as a critical but often overlooked “vitamin”—as the foundation of recognition systems. Trust must exist not only between learners and institutions, but also within the data practices that underpin credentialing, transfer, and assessment. Annual catalog refreshes, unstructured PDFs, and vendor-driven replication of legacy practices were identified as systemic barriers that impose collective costs and inhibit meaningful recognition. 

MyCPL was presented as an example of a staged, human-centered recognition process that blends technology with guided human engagement. By sequencing review, assessment, advising, and confirmation steps, the model supports learners in articulating their experiences while building confidence, affirmation, and persistence. The session argued that affirmation—the human act of being seen and encouraged—is as vital as formal recognition itself, using The Wizard of Oz as a metaphor for exposing the humanity behind institutional authority to recognize and affirm. 

In conclusion, the presentation positioned current disruption not as an existential threat but as an opportunity to reimagine recognition systems that are more inclusive, proactive, and scalable. By balancing reactive and proactive efforts, leveraging structured data, and centering trust and affirmation, recognition practices can expand access, improve mobility, and better serve learners across diverse pathways. 

Keywords: Recognition of Prior Learning (RPL / PLA / CPL), trust, structured data and interoperability, learning mobility and transfer, human-centered recognition systems