Supplier Best Practice

We’d like to share the work that Stone Maiden have been doing to embed a RAG rating system to categorise all of their learners and the amount of support they are likely to require during their learner journey.

The idea is straightforward, each learner is given a rating based on their work ethic, learning level and the suppliers past experience of the individual learner, as shown below:

  • Green = fully committed learner who always attends and will work extremely hard to achieve their qualification by the planned end date.
  • Amber = learner is generally a good learner, but may require some additional support and motivating to achieve their qualification by the planned end date.
  • Red = learner will require lots of additional support and motivating, perhaps lots of chasing and phone calls to ensure that they attend their planned learning sessions. Learner is unlikely to achieve their qualification by the planned end date without a significant amount of additional support.

The idea is that the Centre Managers use the RAG system to allocate additional tutor time, or admin resource (in terms of chasing learners) to those learners that are at greater risk of not achieving their qualifications by their planned end dates.

Management use the raw data on a weekly basis to drive this and tutors are set deadlines by which they should return the information. This is fully implemented now within their standard operating processes and is not just done on an ad-hoc basis.

The benefit of this system is it allows the supplier to proactively identify potential issues with learners and put additional resources in place at a very early stage of the learner journey before a learner becomes an LPED.

Since the system was implemented at the end of 2015, the supplier has seen a significant increase in timely success rates, a large reduction in both the number of learners passed end date (LPEDs) and their withdrawal rate.

Overall it has allowed them to have much tighter control over their learners and dramatically improve their learner data statistics.