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Learning data falls into one of two categories: context data and behavior data. Each is complementary to the other. Consequently, a comprehensive learning data integration from a learning tool to the UDP requires two data integrations: a context data integration and a behavior data integration.
The Student Information System (SIS) provides a context data integration to the UDP. The Learning Management System and Learning tools provide both context data and behavior data integrations to the UDP.
The integration model for each category of data uses distinct data interoperability standards and integration mechanics.
Behavior data, also called event data, describes the discrete behaviors of learners, instructors, and other actors in the learning environment.
Behavior data is emitted in real-time as an event stream from a learning tool. Its event stream is transmitted over the web to the UDP Caliper endpoint. Currently, the UDP supports the IMS Global data interoperability standard for behavioral data.
Context data describe objects (e.g., learners, assignments, modules, outcomes, learning design, course catalog, degree) and relationships relevant to learners, learning environments, and overall academic experience. Context data exists in all tools and systems used by an institution to support its academic mission.
Context data must be generated by Learning tools as CSV-formatted flat files whose contents conform with a UDP Loading schema, which is a subset of the Unizin Common Data Model. A complete context dataset is delivered daily to the UDP via a cloud data bucket, where data is staged for import.
Broadly speaking, any given UDP instance will integrate data from three systems:
- Student Information System (SIS)
- Learning Management System (LMS)
- Learning tools/LTI tools
An SIS data integration is foundational to the implementation of a UDP instance.
A Student Information System (SIS) is a foundational, enterprise solution at all Academic institutions. Among other things, the SIS is responsible for managing the course catalog, student registration, student matriculation, student degrees and transcripts, and many other data that support an institution's administrative processes.
Student information system (SIS) implementations vary widely among higher ed institutions, even among the popular SIS vendor solutions (e.g., Banner, PeopleSoft, etc.). Consequently, there are no packaged, fully-managed integrations between SIS products and the UDP. Institutions must write a context data integration between their SIS and the UDP. The integration must conform with the UDP’s SIS loading schema and context data integration mechanics.
A Learning Management System (LMS) is an enterprise course-delivery software solution at Academic institutions. It is used for the administration, documentation, tracking, reporting, automation and delivery of educational courses, training programs, or learning and development programs.
Learning tools are used every day to enrich the LMS-based course experience. Typically, they integrate with the LMS via the IMS Global LTI standard, and are therefore sometimes called LTI applications. But not all learning tools integrate with the LMS in this way or at all. At any given institution, dozens and even hundreds of learning tools may be used.
Because learning tools deliver teaching and learning experiences, their data is vital to incorporate into the broader institutional learning data landscape. Typically, learning tool UDP integrations are developed by vendors. Ideally, vendors provide both a context data integration and behavior data integration with the UDP. Each integration likely needs to be configured separately.
If the learning tool provider offers an event stream that supports the IMS Global Caliper standard, then the integration with the UDP is plug n’ play.
If the learning tool provider has worked with Unizin to develop a UDP loading schema and context data integration, then consult their documentation to configure a UDP integration. The vendor should conform with the integration mechanics for context data, which involves pushing data into a cloud storage bucket on a nightly basis.