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Understanding trainee withdrawals

We conducted data analysis and desktop research to understand what we know and what we don’t know about why trainees withdraw from teacher training.


In July 2023, DfE published “Initial teacher training (ITT) performance profiles” for the 2021/22 Academic year. It showed that the proportion of ITT trainees who did not complete their training in 2021/22 rose to 7 per cent, up from 5 per cent in the previous year and the highest rate for 5 years. The DfE are unsure as to the reasons for this increase.

The Register trainee teachers (Register) service holds a wealth of information about each trainee including their pathway into ITT and the date of withdrawal as well as extensive demographic information. Providers use the service to record the reason(s) why a trainee withdraws from an ITT course.

A screenshot showing a list of reasons on the Register service for providers to record why a trainee decided to withdraw from the course
A list of reasons on the Register service for providers to record why a trainee decided to withdraw from the course

What we needed to do

We would perform data analysis of the Register service and conduct desktop research of the trainee withdrawals process. We would aim to codify our data to understand what we currently know about who, when and why trainees are withdrawing from teacher training, uncover gaps in our knowledge to inspire further research and lay the foundations for interventions that might improve trainee retention.

What we did

Data analysis

We began by establishing the scope of the study and imposed parameters to ensure we would finish the study in time.

  • We only mapped what is happening. By understanding the current situation better, we can identify which areas are worth exploring more to get to the why

  • We only included trainees if their Academic Year started from 1/8/2022 and ended with a course outcome no later than 31/7/23. Deferred trainees were not included (230 records)

  • We only included Postgraduate routes full-time students

  • We were not looking at annual trends, and we did not include geographic location of ITT providers, disability details, nationality details and ethnicity details. We also did not include placement data

  • We cross analysed ITT factors with trainee demographics

The ITT factors we analysed were provider, route, institution, subject, date and reason. The demographic factors were gender, age, ethnicity, nationality and disability.

We did not analyse the effect of multiple demographics or ITT factors. For example, the withdrawal rates of subjects within different institution types or the effect to the withdrawal rates of gender and ethnicity.

We performed two rounds of analysis on the data:

  1. We tested each population proportion for statistical significance to determine if the rate was notably different from the rate in the overall cohort. For example, whether the withdrawal rate for trainees in High Potential ITT is significantly different from the overall ITT withdrawal rate.

  2. We conducted a cross-checking exercise to look at the interaction between the different combinations of ITT factors and demographics. For example, comparing the withdrawal rate of Asian trainees in HPITT to the overall ITT withdrawal rate.

Desktop research

Alongside the data analysis, we conducted desktop research. We reviewed a variety of articles, forums, and research reports from various sources such as GOV.UK, HESA and DfE departments.

We also contacted UCAS, NowTeach, National Education Union, Student Loans Company, and ITT providers. We sought their assistance in answering questions we had about overall withdrawal rates, the influence ITT pathways and demographic factors have on withdrawals, when trainees withdraw, and their reasons for withdrawal.

What did we learn?

Withdrawal rates have increased in recent years for non-ITT courses and vary across all higher education studies.

Post-Covid, ITT providers told us they find it challenging to determine trends for trainees leaving their course.

We learned where the hotspots for trainee withdrawals are within ITT factors and demographic data.

Summary of findings

  • Trainees in HPITT have a higher-than-expected withdrawal rate

  • Most withdrawals from ITT happen in the first few months of the academic year (October, November and December). This date of withdrawal was largely the same regardless of ITT factor or demographics

A line graph showing weekly ITT withdrawals for the academic year 2022 to 2023. It shows a clear rise in withdrawals in the first three months for all cohorts gradually reducing in number until the end of the academic year
ITT withdrawals by date
  • ‘Another reason’ accounts for over half of all reasons given and was given by all demographics

  • Males have higher than expected withdrawal rates

  • Older (36+) trainees withdraw more, but those who are 21-25 gave different reasons to other age groups

  • Asian ethnicities have higher than expected withdrawals overall and by institution type (SCITT) and route (HPITT)

  • Non-British TTs have higher than expected withdrawal rates for HPITT (route) and give different reasons to British trainees

  • Trainees with a disability have higher withdrawal rates overall and by institution type (SCITT) and route (HPITT). They also give different reasons to those without a disability and ‘does not want to become a teacher’ is only 1.15% of the reasons given by those with a disability

Next steps

We will use this research as an evidence-based foundation for more focussed analysis into trainee withdrawals.

We will aim to understand more about withdrawals by speaking with providers and trainees about their ITT experience. In combination with our colleagues in DfE we will create an experience map of the withdrawals journey to gain insight into the pain-points in ITT which may lead to more trainee withdrawals.

We will consider a service for trainees in Register which will allow the DfE to gather better data and insight into why trainees are withdrawing.

We will create a dashboard for withdrawals in Google Looker Studio so that data analysis can be easily accessed by anyone who needs it.