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Monitoring early leavers

Why is it important to monitor early leavers?

Determining the whereabouts of early leavers, as swiftly as possible, increases the chances that they reengage.

Once learners drop out, the longer they stay outside education and training:

  • The more difficult it will be to re-join former classmates, and the more likely it is they will have to join groups of students younger than them, unless alternatives are in place;
  • The more likely the young person will be involved in activities other than education and training, such as unqualified employment; and
  • The higher the chances that the young person loses basic routines (e.g. following a daily schedule of activities) needed to integrate in a programme and succeed.

All these issues often act to demotivate young people to return to education and training. However, in some cases, in particular when the young person is employed, a period of drop-out can also help to mature and gain motivation to return to education and training.

Education and training providers have information on the students who drop out of their programmes, or leave the school or training centre before attaining a qualification. However, they usually do not have the necessary information to verify if the young person is an early leaver, has enrolled in a programme elsewhere, or is employed.

A centralised monitoring system can help follow up early leavers with more accuracy.

How can a centralised monitoring system be set up?

The following tips are given as advice to policy makers at national or regional level aiming to introduce a centralised system to monitor early leavers. The information is based on Cedefop research into existing monitoring systems.

Tip 1. Establish a centralised monitoring system that provides nominal information on early leavers

There are different types of such monitoring systems:

Option A: Cross-referencing enrolment data from different education and training providers

It is possible to detect early leavers by comparing enrolment data from the different education and training providers. The aim is to identify young people who are not enrolled in any education and training programme and have not attained a qualification.

This requires the cross-referencing of administrative data from various education and training sub-systems (significantly, between school-based VET and apprenticeship schemes) and networks of providers.

This cross-referencing can present significant technical challenges. The use of student registers based on individuals’ personal identification (unique student number) hugely facilitates the task.

Option B: Student registers based on individuals’ personal identification

Each learner has a unique identification number. This makes it very easy to cross-reference administrative data from various education and training sub-systems and providers, and to identify who has left the education and training system.

However, data protection regulations may pose some challenges to the implementation of such systems.

Option C: Centralised register of early leavers

Every education and training centre has to report all drop outs to a central service that keeps a specific register on early leavers.

Tip 2. Periodically update and review centralised monitoring systems

Data in centralised monitoring systems needs to be regularly updated and reviewed to make sure that the time elapsed between drop-out and the moment young people are contacted is as short as possible.

Tip 3. Establish a process that determines which organisation (and within it which person) is responsible for reaching out to the individual

Monitoring systems need to be complemented by procedures to ensure that each person identified as an early leaver is contacted as soon as possible. There can be a centralised service in charge of contacting early leavers, or a coordination structure involving different stakeholders in this task.

The approach adopted when reaching out to early leavers needs to take into account the specific characteristics of each young person. In particular, early leavers facing significant barriers to education, including those with complex life situations, are more easily reached through community-based interventions. These young people often do not have trust in people who represent ‘the system’ so people who are closer to their community may be better placed to help them re-engage. This can involve street work (for instance, a youth educator could visit the young person at his/her house or during outdoor activities with other young people).

Tip 4. Link the identification of early leavers with the necessary measures

The services or structures contacting early leavers are in charge of:

  • Verifying if the young person is indeed an early leaver (to exclude for instance those who changed residence to another country; employed youths could also be excluded);
  • Check if the young person is already receiving support;
  • Collect information on the characteristics of the young person and his or her needs to be able to offer tailored support;
  • Coordinate the different services and education and training providers to offer an adequate response to each young person.

Tip 5. Be aware of data protection issues

Personal data protection is a major concern when developing monitoring systems. Such systems need to comply with legal frameworks for data protection and ensure that:

  • Data gathered is used for a legitimate purpose: to provide appropriate support to young people who left education and training.
  • Data is only accessed by professionals who have an immediate role in providing this support.
  • Personal data is only stored for as long as it serves its purpose, that is, to provide support to a particular young person.

Data can also be used in research. For this purpose, data needs to be anonymised. Specific ethical guidelines in using the data should be acknowledged by all actors involved.

Tip 6. Collect information on individuals’ characteristics as well as their education pathways, with research purposes in mind

Information on individuals’ characteristics and their education pathways can greatly contribute to the analysis of the problem of early leaving in a country. A good comprehension of the phenomenon requires collecting data that allows to establish:

  • Who is leaving the system, based on information on the sociodemographic characteristics of the learners?
  • When are learners leaving the system?
  • Where are they dropping out from?
  • Why are they leaving the system?
Who is leaving the system? When are early leavers leaving the system? Where are early leavers dropping out from? Why are young people leaving the system?
  • Age
  • Gender
  • Migrant or ethnic minority background
  • Other (e.g. disability)
  • Early leavers who do not finish lower secondary education
  • Early leavers who finish lower secondary education but do not make the transition to upper secondary education
  • Drop outs from upper secondary (per year of the programme)
  • Early leavers who completed a short upper secondary programme (ISCED 3c)
  • Drop outs who complete an upper secondary programme but fail the final exam
  • General programme/VET
  • Type of programme (e.g. school-based VET/apprenticeships)
  • Field of study
  • Health and well-being issues or conditions
  • Family responsibilities
  • Non-availability of work-based learning opportunities or apprenticeship placements
  • Disliked programme, VET provider, staff, or peers
  • Found a job
  • Financial problems in the family
  • Etc.

Tip 7. Use monitoring data to assess the effectiveness of measures to tackle early leaving from education and training

The monitoring of early leavers helps collect useful data for the evaluation of measures. For instance, to analyse whether participants in different support measures have ultimately completed upper secondary education.

Centralised monitoring systems and systematic procedures to contact early leavers can improve stakeholder cooperation and measure coordination

The availability of monitoring systems to track early leavers, and services in charge of contacting them, facilitate information sharing, cooperation between the relevant stakeholders, and a better coordination of reengagement measures for early leavers. It can also contribute to:

  • Increasing early leavers’ interest in education and training providers;
  • A better understanding of the process of disengagement and the factors that lead to early leaving among practitioners; and
  • A better acceptance of irregular education and training pathways and of the diversified solutions available for learners.
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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.

Good practice

Local Action for Youth (ALJ, Action locale pour jeunes)

The Luxembourgish Local Action for Youth can make direct contact to help early leavers from education and training thanks to the national register of pupils updated monthly by the Education Ministry.

Good practice

The drive to reduce drop-out rates

Based on data from a centralised database, the Dutch Ministry of Education, Culture and Science provides monthly and yearly reports on Early School Leaving (ESL). Based on this data, new policies are developed at ministerial level to tackle ESL.

Study

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.

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Study

Evaluation partenariale de la politique de lutte contre le décrochage scolaire

Would you like to read about the benefits of coordinating different stakeholders in the monitoring of early leavers? The 2014 evaluation of policies against early school leaving in France discusses the impact of the Platforms for monitoring and coordination of early leavers (PSAD).

Read the report here (in French)

Data

ET monitoring 2016

In 2016, the EU-28 average rate stood at 10.7%. The situation varies considerably across member states (see figure). Malta (19.6%), Spain (19%), and Romania (18.5%) have the highest rates of early school leavers. The lowest rates exist in Slovenia (4.9%), Lithuania (4.8%) and Croatia (2,8%) although for this last, the numbers are of limited reliability according to Eurostat.

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