ILR - Youth-Transitions/resources GitHub Wiki

Background

The Individualised Learner Record (ILR) is an umbrella term for a series of data collections relating to individuals in Further Education (FE) institutions in England.

In LEO, researchers are provided with the Longitudinal Individualised Learner Record (LILR), which is assembled (outside of LEO) from individual ILR datasets. Some preprocessing has already taken place to add some useful derived fields and provide consistency in variables that have changed over time (where possible).

Source LILR contains three views:

  • Learner provides information about individual learners. Note, however, that these are not necessarily distinct real-world individuals since an individual who attends multiple FE providers across multiple years will have multiple learner records (and identifiers).
  • Aims provides information about the programmes and learning aims that learners are pursuing in the FE sector, including work experience and enrichment as well as formal qualifications.
  • LARS (Learning Aims Reference Service) provides reference data about learning aims, including type, level and subject, though many of these fields also appear on the aims view

LILR also includes reference data on providers, containing, for example provider type (FE College/ work-based learning provider, local authority etc.), but this is not usually included in LEO.

Processing

The first stage of the aims processing is simply to load the data into a format that makes working with the data easier. This involves:

  • Adding person identifiers (so that records can be joined to other datasets)
  • Putting indexes on the identifiers
  • Setting appropriate (and minimal) data types for each variable

While some of these steps might appear to be solely for the purpose of performance or efficiency, we have observed issues using ILR in particular where inefficient statements fail to complete without error.

We tend to work with a subset of the variables provided in ILR. These have tended to be sufficient for most of the work we have done with ILR in the past but there will almost certainly be occasions when other variables might come in useful.

Example uses

Our main use of ILR has typically been to track post-16 education and training in the FE sector. A combination of PLAMS and School Census can be used to do similar for the school sector.

It can also be used alongside HESA data to track post-19 education and training.