Background and Motivation
The xAPI specification provides a powerful way for applications to interoperably store and retrieve data about learning activities of users or groups of users. XAPI terms are not natural language words, but approaches to handling natural language ambiguity inform approaches to handling xAPI ambiguity. Therefore, the key driver for providing additional vocabulary guidance for xAPI is to improve consistency in the meaning of terms. When considering the semantic meaning of natural language words, there are two fundamental challenges as stated in ANSI/NISO Z39.19-2005 (2010):
- Two or more words can be used to represent a single concept (e.g., verbs such as “finished” and “completed”).
- Two or more words that have the same spelling can represent different concepts (e.g., verbs such as passed (to physically move past) or passed (successfully pass an examination)).
In order to address similar challenges that arise when implementing xAPI, a semantic data model and strategy for more accurately describing vocabulary terms is needed. The World Wide Web Consortium’s (W3C) Resource Description Framework (RDF) Linked Data (LD) technology stack was identified as a viable way to address this need. RDF provides a common data model and standardized syntax for identifying and describing entities (e.g., “Things”) or concepts in the world. In addition, LD compatible with JSON (JSON-LD) was released in 2014 which was naturally of interest to the xAPI community--since xAPI was already based on JSON.
By adopting LD practices for xAPI vocabularies, the xAPI can achieve a whole new level of semantic precision. Upon implementing this companion specification (and refinement of vocabulary publishing practices and processes), the xAPI community can also benefit from new capabilities generally inherent in RDF. In terms of practicality, it can immediately provide improved meaning, multilingual translation, discovery, and reuse of xAPI vocabulary terms. Conceivably, it will also open up new doors for federated search, dynamic look-up of vocabulary data within authoring applications, improved learning analytics, machine learning, and adaptive learning capabilities supported by intelligent agents and other systems.