This knowledge area covers Data Instrumentation, a fundamental knowledge area in learning engineering that involves collecting, processing, and managing data to support learning solutions effectively. This process leverages standards-based infrastructures, hardware and software sensors, data pipelines, learning record stores (LRSs), structured data stores and other technology components. Instrumentation ensures that the data collected from learning environments is consistent, reliable, secure, and usable for improving learning outcomes.
This knowledge area details generally accepted components of data instrumentation in learning engineering, including architectures that use standards like Experience API (xAPI) (IEEE 9274.1.1), metadata tagging (IEEE 2881), and machine-readable competency definitions (e.g. IEEE 1484.20.3), as well as the architectural stages involved in processing data for different uses.