Learning Analytics Interoperability

The ease with which data can be transferred without loss of meaning from a store to an analytical tool – whether this tool is in the hands of a data scientist, a learning science researcher, a teacher, or a learner – and the ability of these users to select and apply a range of tools to data in formal and informal learning platforms are important factors in making learning analytics and educational data mining efficient and effective processes. I have recently written a report that describes, in summary form, the findings of a survey into: a) the current state of awareness of, and research or development into, this problem of seamless data exchange between multiple software systems, and b) standards and pre-standardisation work that are candidates for use or experimentation. The coverage is, intentionally, fairly superficial but there are abundant references. The paper is available in three formats:  Open Office, PDF, MS Word. If printing, note that the layout is “letter” rather than A4. Comments are very welcome since I intend to release an improved version in due course.

One thought on “Learning Analytics Interoperability

  1. I’ve since come across a suite of technical specs for Statistical Data and Metadata eXchange (SDMX) – http://sdmx.org/?page_id=10 – and the W3C Data Cube working draft – http://www.w3.org/TR/vocab-data-cube/ – which is compatible with it. SDMX is used by Eurostat (among others), who offer SDMX as a bulk download format and have tutorial-style info: https://webgate.ec.europa.eu/fpfis/mwikis/sdmx/index.php

    Google’s DSPL (DataSet Publishing Language) is essentially a simplified and more developer-friendly subset of SDMX: https://developers.google.com/public-data/overview. There is an EUPL licenced converter.

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