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Identification of learners at risk of early leaving

Why is it important to timely recognise the signs of early leaving?

Learners at risk of early leaving present different distress signs often a long time before they leave. If these signs are detected in a timely manner, there are more chances of reengaging young people with relatively simple interventions. An early intervention allows for better results with fewer resources.

Each learner is different and so are his or her ways to show that something is not going well. Absenteeism, low attainment, and disruptive behaviour in the classroom are often linked to potential early leaving. Other signs such as emotional distress can easily go unnoticed.    

Practitioners are best placed to spot pupils at risk as they track absenteeism and academic attainment in their daily work. The fact that they are in direct and regular contact with the learners also puts them in the best position to spot distress. However, they often do not have the ability, time or resources to identify and act upon signs of risk. The use of a systematic approach to identify learners at risk of early leaving by education and training providers is the first step to tackling early leaving.

How can VET providers identify learners at risk of early leaving?

The following tips are given as advice to education and training providers aiming to develop or improve their systems to identify learners at risk of early leaving. The information is based on Cedefop research into existing early warning systems.

Tip 1. Choose relevant indicators

Early warning systems are based on the selection of indicators associated with risk factors. These indicators can cover information on family background; information on attainment, behaviour and attitudes at the education and training institution; and information on health and well-being. The following table collects the main indicators found in literature. VET providers can choose to use some or all of the indicators shown:

Family environment

Attainment, behaviour and attitudes

Health and well-being

  • Socio-economic status of family
  • Migrant or ethnic minority background*
  • Family responsibilities  (e.g. taking care of siblings)
  • Lack of family engagement and support



  • Education underachievement (poor grades; grade repetition)
  • Absenteeism
  • Disruptive behaviour or lack of positive involvement in activities within or outside the curriculum
  • Negative self-perception linked to education failure
  • Absence of positive future vision of oneself
  • Lack of work readiness


  • Health circumstances (illness, substance use, pregnancy)
  • Issues related to child poverty (e.g. hunger, lack of sleep)
  • Issues related to personal, social and emotional well-being (e.g. sense of belonging to the training community; satisfaction with student-teacher relations; relations with peers; bullying)


* In some countries it is not possible to collect this information due to legal provisions.

The efforts and time invested in the development of an early warning system depend on the degree of sophistication of the system.

A systematic monitoring of a small set of indicators, for which there are already administrative data (this is often the case for attendance and attainment), can be a relatively simple step for many education and training providers.

Although designing and developing a more comprehensive early warning system, covering a wider set indicators (e.g. including issues related to personal, social and emotional well-being), can be more resource intensive, it is also more effective at identifying learners at risk.

Tip 2. Involve the team of practitioners in data collection and analysis

Practitioners are best placed to collect data on the above indicators. Teachers and trainers as well as counsellors and other professionals, can access such information based on observation and discussions with students. Practitioners can also use tools, for instance questionnaires, to collect data on the less visible signs of risk (e.g. students’ well-being).

Practitioners are also key actors in data analysis and in choosing the most adequate responses for each learner. To facilitate their involvement in this phase, it is important to make data easily accessible to them.

Tip 3. Build practitioners' capacity to develop and use an early warning system

Practitioners need support from national and regional administrations, mainly for:

  • The design of early warning systems. For instance, guidance on what indicators to use and on personal data protection;
  • Data analysis and how to use data to inform the design and implementation of measures.

Tip 4. Link the identification of at-risk learners with the necessary measures

The identification of a learner at risk must trigger an intervention. Education and training providers need to organise periodic team meetings to analyse data and decide on appropriate measures.

Tip 5. Be aware of data protection issues

Personal data protection is a major concern when developing an early warning system. Such systems need to comply with legal frameworks for data protection and make sure that:

  • Data gathered is used for a legitimate purpose: to provide appropriate support to learners at risk of early leaving.
  • Data is only accessed by professionals who have an immediate role in providing this support. Only practitioners working with a certain student should have access to his/her data. Also, there can be different levels of access. More sensitive data (e.g. on health, well-being) should only be accessible to a very restricted group of practitioners (e.g. head teacher and counsellor).
  • Personal data is stored for as long as it serves its purpose, that is, to provide support to a particular learner.

VET providers need to ask for permission of students in registration forms to use the data, including by third parties.

Part of the data can be transmitted to national or regional level authorities to develop indicators at that level (for instance, this is often done with data on absenteeism), or be used in research. For these purposes, data needs to be anonymised.

Tip 6. Use early warning systems data to assess the effectiveness of measures to tackle early leaving from education and training

Systems for the identification of at-risk learners can also be useful in the evaluation of measures. For this, such systems need to combine periodic data on risk indicators and information on the programmes attended by learners, and support measures applied. This would allow for the assessing of a particular measure to ascertain whether or not it had the expected impact on risk factors.

What is the potential impact of early warning systems?

Early warning systems imply the use of data for a timely identification of at-risk learners. These systems also promote:

  • Information sharing and cooperation between practitioners
  • A better coordination of measures for at-risk learners at provider level
  • A better understanding of the process of disengagement and the factors that lead to early leaving among practitioners. 
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Quick win

Using data to identify at-risk learners

A VET school in the Centre Region of Portugal has created an internal electronic monitoring system to monitor truancy.

Quick win

Using data to make the right decisions

In the city of Hasselt in Flanders (Belgium) data on school absenteeism is used to inform school-specific action plans.


School-based Prevention and Intervention Measures and Alternative Learning Approaches to Reduce Early School Leaving

This study shows that early warning systems usually cover more visible cognitive and behavioural indicators like students’ grades, truancy or transgressive behaviour. This causes at-risk students who do not display such signs to remain undetected. The authors insist on the need to also monitor students’ emotional well-being.



Developing indicators for early identification of young people at risk of temporary disconnection from learning

Would you like to know more about what type of indicators are being used in practice?

The UK Local Government Association commissioned a study that has analysed the indicators used by English local authorities to identify young people at risk of becoming youth not in employment, education or training (NEET).

You can read the full study



ET monitoring

Education and training monitor 2017
A yearly evaluation of education and training systems across Europe.

Keeping young people in education longer: steady progress in Europe
The share of early leavers from education and training continues to fall, with a growing number of countries that have reached the ET 2020 target and their own national targets. The ELET share is lowest for women, while non-native people and young people in rural areas show higher ELET rates.

Young people from a migrant background at greater risk of leaving education and training early
In the EU, more than one in four young people from a migrant background leave education and training too early.

Toolkits and tools

Stay@School - The School Inclusion Project Transfer of Innovation. Educational Products.

The project Stay @ school developed several tools to help teachers assess the risk of school dropout, including questionnaires for students, teachers and parents.

Toolkits and tools

LYCAM (Lycée, ça m'intéresse) questionnaire

The LYCAM (Lycée, ça m'intéresse) questionnaire, developed by the French Ministry of Education aims at helping practitioners to identify secondary school students’ difficulties, motivations and personal views of school.

Toolkits and tools

Training programme for VET teachers developed within the framework of the STAY IN project

The STAY IN project proposes a training programme for VET teachers and youth workers. Its module 1 discusses indicators to identify learners at risk of early leaving

Toolkits and tools

Qualifizierte Ausbildungsbegleitung in Betrieb und Berufsschule (QuABB) – Capacity building for students, companies and vocational schools involved in apprenticeship-training (QUABB)

The project QuABB (Capacity building for students, companies and vocational schools involved in apprenticeship-training) provides a collection of tools for the identification of apprentices at risk of early leaving, and guidance to trainers, VET teachers and parents to deal with this situation.