Student outcome and experience measures

To help higher education providers and other users to understand the methods that we use to produce student outcome and experience measures, we have published a series of documents providing:

• Descriptions of the measures and the methods used to construct and present them, which can be used to better understand our approach.
• Technical descriptions of the categorisations (or algorithms) applied to individualised student data records to generate our measures.
• Instructions for providers to rebuild our student outcome and experience measures from their individualised data files, which providers can use alongside the algorithms document to understand which students have contributed to our measures, and how those students contributed.
• The sector averages that our benchmarking calculations use to construct provider indicator benchmarks, which can be used to understand how the outcomes and experiences of a providers students compare to similar students in the sector as a whole.

The documents published here relate to the outputs published in 2023 in support of our work on access and participation, regulation of student outcomes and the Teaching Excellence Framework (TEF). They align with the approach described in our consultation on the construction of student outcome and experience indicators. We update these documents every year. Editions of these documents produced in earlier years remain available in the archive below.

2023 algorithms

Algorithms for student outcomes and experience measures

• Description and definition of student outcome and experience measures used in OfS regulation
  This publication includes 2023 core algorithms and subject code mappings
• 2023 rebuild instructions for student outcome and experience measures
• Benchmarking sector averages

While all of the algorithms described in these documents are relevant to production of our student outcome and experience measures, we also use some of them in other analysis and statistics. For example, algorithms found in our core algorithms documents are used as the basis for the definitions of published data on the student characteristics data and our own key performance measures.