The government announcement that apprenticeship qualification achievement rates (QAR) climbed back to 60.5 per cent last year was understandably greeted positively, although the sector recognises that there is still plenty of room for improvement.
The debate has long moved on from simply laying the blame for non-completions at the door of providers. Research in the past five years has identified various reasons for apprentices withdrawing, ranging from the positive such as moving to a better-paid job to the negative such as lack of employer support.
These research projects are only occasional, however, and given the importance of apprenticeships, we need to deploy more regular means to identify reasons for non-completions and implement actions to further improve the QAR.
When a learner withdraws from an apprenticeship, there is a Department for Education requirement to capture within the ILR the reason from a set of limited categories. Although training providers may be aware of the reason, the most commonly used category is “other” followed by “other personal reasons”, which between them account for almost 90 per cent of withdrawals and does not tell us much at all.
The reason offered by the early leaver may be given directly to the development coach, which does not guarantee honesty. Hence the ILR withdrawal categories do not allow for an informative analysis of why learners withdraw and how this might vary across apprenticeship levels and ages.
While still mapped to the ILR codes, Aptem’s customer providers can create detailed custom categories to track withdrawals which ensure a richer data set and result in more effective learner improvement plans.
We recently undertook a thematic analysis of withdrawal reasons used by our customers, clustering these into 23 categories. We did this by analysing the available data for learners withdrawing from an apprenticeship on or after August 2023 to March 2025. This gave us 35,190 data points across 140 training providers, of whom a third are using custom reasons to capture the reasons.
We found that a third of withdrawals were employment status-related, where the learner’s employment was either terminated, the learner made redundant, they resigned or were promoted to a new role not suitable for their apprenticeship programme. Some 6.3 per cent of learners withdrew due to an employer no longer supporting the apprenticeship and withdrawing or not actively providing support within the workplace.
A further 15 per cent of apprentices withdrew due to a lack of commitment or engagement, poor attendance or simply no longer wanting to continue with their apprenticeship. Another 4 per cent withdrew because the course/programme was unsuitable for them, or they had decided to study in further or higher education instead.
Perhaps surprising given the case for the recent policy changes, the analysis only identified 4 per cent leaving due to dissatisfaction with or failing to complete functional skills requirements – although this rises to 7 per cent for level 3 apprentices. We would expect the recent scrapping of functional skills requirements for adult apprentices to lead to this small proportion reducing further over the next academic year.
At lower levels, apprentices were more likely to leave for employment status-related reasons compared to their higher-level peers, with those on degree level apprenticeships such as nursing and policing being on clear employment and progression pathways.
Apprentices aged 16 to 18 were more likely to withdraw for employment status-related reasons (47 per cent) compared to any other group. Often this was due to a change of employer.
Older learners were significantly more likely to withdraw for personal reasons or commitment and engagement factors. Compared to younger learners, those aged over 35 were almost twice as likely to withdraw for health-related reasons.
The use of custom reasons therefore offers more detail on why learners withdraw. It can identify appropriate interventions such as learner motivation, wellbeing support and better employer engagement.
Improved retention will obviously lead to an increase in success rates, while the richer data can evidence positive progression, such as when a learner leaves to move into further study. All this offers a much more nuanced picture of outcomes, beyond the QAR.
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