• Improving Human Performance Outcomes depends on the provisioning of learning resources to the individual at the appropriate opportunity.  When scaled to an entire workforce, logistical challenges may arise and optimization methods should be deployed.  In order to have technology, including artificial intelligence, act as the intermediary for opportunity and optimization, the appropriate amount of data, particularly metadata, about Learning Resources and their corresponding events is required.  

When Courseware Based Training (CBT) became popular in the late 1990s metadata was used, usually unsuccessfully, to create repositories of Learning Resources that were intended to be shared across Communities of Practice (COP).  Recent efforts in metadata standards, coupled with the advancement of AI, have re-vitalized COPs to attempt to define and enable use cases for learning-based metadata.

This tutorial will describe the learning ecosystem that can be created by metadata and how current standards can be leveraged for success.  Specific use cases that can be met through the use of metadata will be described and solutions presented.  These use cases include, but are not limited to search, discovery, application within learning, optimization of both learners and the resources themselves, and lifecycle management of learning resources.  The landscape of available metadata standards, and particularly how they can be combined, will be described in great detail and attendees will have the opportunity to model such solutions in accordance with these standards.  These standards are centered around the LRMI vocabulary from the Learning Resource Metadata Innovation (LRMI) workgroup of Dublin Core Metadata Initiative and IEEE Learning Metadata Terms (P2881) efforts but will include other metadata standards and are applicable beyond.  The benefits of using Resource Description Framework (RDF) best practices will be described and realized in the tutorial and accompanying learner-created metadata graph.

P2881 is an effort created by those familiar with legacy metadata standards used in the Shareable Content Object Reference Model (SCORM) and how those failed in application.  P2881 attempts to define a small core model applicable to all types of Learning Resources that is applicable to solving particular use cases and leaving the further definition of types, such as “courses”, to respective COPs.  A core component of P2881 is the distinction between Learning Resources and Learning Events.  Learning Resources are defined by LRMI and have been thoroughly defined and accepted the standards community. Learning Events are instantiations or opportunities of Learning Resources that are bound by time, materials, and human capital.

Keywords
ADAPTABILITY, ADVANCED DISTRIBUTED LEARNING, AI, AUTOMATION, CLASSIFICATION, DATA, EDUCATION, ELEARNING, INTEROPERABILITY, LEARNING STANDARDS, LEARNING TECHNOLOGY STANDARDS, OPEN STANDARDS, STANDARDS