Problem statement

Addressing problem: Recognising the signs of early leaving from education and training

Learners at risk of early leaving often present distress signs long before they leave. If these signs are detected in a timely manner, there are more chances of re-engaging 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 of showing that something is not going well. Absenteeism, low academic 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 recognize distress signals and spot pupils at risk as they are in direct and regular contact with the learners and they track absenteeism and academic attainment in their daily work. 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 identifying learners at risk of early leaving by education and training providers is the first step to tackling early leaving.

Addressing the problem

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

The following tips intend to help education and training providers develop or improve their systems for identifying learners at risk of early leaving. The information is based on Cedefop research into existing early warning systems.

Tip 1. Define the purpose of data collection

It is important to define the purpose/s of data collection before choosing indicators and determining how to collect data. The main purpose of early warning systems is to identify learners at risk of early leaving as to be able to provide them with timely and appropriate support, but data collection can serve additional purposes, such as:

  • Monitor the risk of early leaving at institutional, local, regional or national level
  • Assess the effectiveness of measures to prevent early leaving
  • Increase knowledge on the protective and risk factors linked to early leaving

For instance, for (some) data to be comparable over time and be used for monitoring or for the evaluation of measures, one could consider:

  • Including indicators for which there are already administrative data.
  • Develop tools to collect additional data, and use them consistently over the monitoring or evaluation period.
  • Include additional indicators and develop additional data collection tools that can evolve over time and can be adjusted whenever necessary to ensure timely identification of early leavers.
Tip 2: 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 the literature on early leaving. VET providers can choose to use some or all of the indicators shown:

  • Socio-economic status of family
  • Migrant or ethnic minority background*
  • Family responsibilities  (e.g. taking care of siblings)
  • Lack of family engagement and support
  • Academic 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 abuse, 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 already are 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 3. Involve the team of practitioners in data collection and analysis

Practitioners are best placed to collect data on the above mentioned 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 practitioners’ involvement in this phase, it is important to make data easily accessible to them.

Tip 4. 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 5. Provide incentives for VET providers who monitor at-risk learners

National and regional administrations can promote the identification of learners at risk of early leaving by incentivising VET providers’ efforts in this field. This can be done in different ways, for instance:

  • Recognise the time spent by practitioners on the design and development of early warning systems and data collection. Including these tasks within practitioners’ regular activities and working hours helps avoid that they are perceived as burdensome.
  • Reward VET providers with good quality early warning systems by providing additional budget for the monitoring of at-risk learners.
  • Acknowledge the work of VET providers by showcasing good quality early warning systems in national or regional events or good practice repositories.
  • If the data collected by providers is used for monitoring at local, regional, or national level, provide useful and timely feedback to providers (e.g. on their position in different indicators compared to average, or compared to providers of similar characteristics).
Tip 6. 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 7. Protect the confidentiality of personal data

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.

An adequate follow-up of learners at risk of early leaving may require sharing data with other relevant services (e.g. social services, health services, youth services, or other). Before exchanging any data, VET providers need to establish if this is allowed by data protection legislation. Usually, data sharing will require arrangements to be put in place, including:

  • Asking students for permission in registration forms to use the data, including by third parties, and to use personal codes/identifiers to link young people’s records in different systems. Before giving their consent, students need to receive clear information on how data might be shared and used by other entities.
  • Signing protocols of collaboration to enable databases to be shared and used by various organisations. The development of protocols for data sharing can be a technically complex and lengthy process. It requires political leadership to bring all actors on board, and close collaboration between policy officers and technical staff from statistical and IT backgrounds.

Data should only be used by organisations providing support to young people, or by researchers and evaluators in charge of assessing if the support provided is beneficial. 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. Specific ethical guidelines in using the data should be followed by all actors involved.

In many countries, it is not possible to share data on learners or contact young people. In these cases, the use of anonymised data for research on the factors related to early leaving, can enable the design of more targeted measures.

Tip 8: 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 used in the evaluation of measures. For this, such systems need to combine regularly collected data on risk indicators and information on the programmes attended by learners, and support measures applied. This would allow to assess whether or not a particular measure had the expected impact on risk factors.

Expected outcomes

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

  • Design timely and tailored interventions to support learners at risk;
  • Better coordination of measures for at-risk learners at provider level;
  • Systematic information sharing and increased cooperation between practitioners;
  • Better understanding of the process of disengagement and the factors that lead to early leaving among practitioners.

Related resources

    Statistics and data
    Statistics and data

    The Education and Training Monitor presents a yearly evaluation of education and training system across Europe. The report brings together the latest data, technical reports and studies, as well as examples of policy measures from different EU countries. The 2019 Monitor analyses the targets and benchmarks adopted under the strategic framework for European cooperation in education and training Education and Training 2020.

    Statistics and data

    The dropout from VET rate is higher for migrant or ethnic minority students than other students in several EU countries.

    Statistics and data
    European Parliament, Infographic Lifelong Learning Briefing

    Young adults whose highest level of education is at or below lower secondary school level are considered early leavers from education and training.

    Good practices
    Good practice

    CroCooS – Prevent dropout! project identified elements of a comprehensive institutional early warning system (EWS) and tested its applicability in national contexts. The final product of the project is the CroCooS Knowledge Centre, which is a complex system linking closely the Resource Pool (theoretical background in easy-to-understand language), the Guidelines (how to build an institutional EWS) and the EWS-Toolkit (several tools can be used in the school to prevent dropout).

    Good practice

    Supporting educational and social inclusion of young early leavers and those at risk of early leaving through mechanisms of orientation and tutorial action.


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

    Lycée, ça m'intéresse

    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.


    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

    Qualifizierte Ausbildungsbegleitung in Betrieb und Berufsschule (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.


    This tool has been developed to assist secondary schools to identify students at risk of becoming NEET once they leave compulsory education.


    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. Download the report here.


    Information 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 here.

    Needs analysis report

    This report analyses the inequality in learning and employment outcomes of native born youth and youth with a migration and Roma background. 

    Download the report here.



    This tool reviews the approaches used by countries of the European Union to profile disadvantaged young people trying to enter the labour market. The objective of the tool is to identify the features of profiling systems that are effective in supporting disadvantaged young people in finding employment.

    Download the publication here.

    Antoni Cerdà-Navarro, Francesca Salvà-Mut, Rubén Comas-Forgas & Mercè Morey-López

    This article looks at the differences and similarities between Spanish-born and immigrant students enrolled in the first year of Intermediate Vocational Education (IVET) programmes in Spain.

    Quick wins
    Quick win

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

    Quick win

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