Learning Analytics Interoperability – Looking for Low-Hanging Fruits

Abstract

When Learning Analytics is seeking a wide community, the challenge of efficiently and reliably moving data between systems becomes important. This paper gives a summary of the current status of Learning Analytics Interoperability and proposes a framework to help structuring the interoperability work. The model is based on a three dimensional Enterprise Interoperability Framework mapping concerns, interoperability barriers and potential solutions. The paper also introduces the concept of low-hanging fruits in prioritising among solutions. Data gathered from a small group of Norwegian stakeholders are analysed, and a list of potential interoperability issues is presented.