Foreword
Purpose
This document sets out the details of data about Further Education (FE) Students, and their learning that must be collected by colleges, independent training providers and local authorities contracted by the West Midlands Combined Authority (WMCA).
Introduction
This document provides a technical specification of ILR data collection requirements, for those who make data returns, implement data specifications and design information systems; including management information (MI) managers, software writers and suppliers.
The data collected is used to calculate funding due to FE providers, for performance monitoring, future planning and to ensure that public money is being spent in line with WMCA priorities.
The funding model referred to within this document is for Funding Model 35 and will reference those Students only with a WMCA home postcode at the start of each learning aim undertaken. The source of funding for WMCA funded Students is 112.
The information here does not cover information already provided to providers by the ESFA or WMCA
within the following, but not limited to:
ILR specification, validation rules and appendices 2021 to 2022
Provider Support Manual 2021/22 7.2. WMCA
WMCA AEB Funding Rules
WMCA Postcode ONS
WMCA AEB Payment and Performance Management
Please read the requirements for Funding Model 35 in the above documents before reading the following document.
This guidance will go through how the WMCA will require the provider to code specific fields in the ILR depending on if the aim is being funded via grant (non-procured) or procured, colleges or competitive tender (Independent Training Providers).
The document will refer to the use of Devolved Area Monitoring (DAM) codes. These codes will be used to monitor the delivery of specific priorities in the WMCA. They are similar to Learning Delivery Monitoring (LDM) codes (used for ESFA funded Students) but these are required for use with aims funded by combined authorities only. There will be six DAM code fields available to be used against each learning aim.