Learning Analytics is now moving from being a research interest to a wider community who seek to apply it in practice. As this happens, the challenge of efficiently and reliably moving data between systems becomes of vital practical importance. System interoperability can reduce this challenge in principle, but deciding where to drill down into the details will be easier with a view of the “big picture”.

Part of my contribution to the Learning Analytics Community Exchange (LACE) project is a short briefing on the topic of learning analytics and interoperability (PDF, 890k). This introductory briefing, which is aimed at non-technical readers who are concerned with developing plans for sustainable practical learning analytics, describes some of the motivations for better interoperability and outlines the range of situations in which standards or other technical specifications can help to realise these benefits.

In the briefing, we expand on benefits such as:

• efficiency and timeliness,
• independence from disruption as software components change,
• adaptability of IT architectures to evolving needs,
• innovation and market growth,
• durability of data and archival,
• data aggregation, and
• data sharing.

Whereas the focus of attention in learning analytics is often on data collected during learner activity, the briefing paper looks at the wider system landscape within which interoperability might contribute to practical learning analytics initiatives, including interoperability of models, methods, and analytical results.

The briefing paper is available from: http://laceproject.eu/publications/briefing-01.pdf (PDF, 890k).

LACE is a project funded by the European Commission to support the sharing of knowledge, and the creation of new knowledge through discourse. This post was first published on the LACE website.