Oma linja school intervention

The Oma linja -intervention aims to help students in their final year of compulsory education (9th graders) to choose secondary education suitable for them. We examine two versions of the program. The "intensive" intervention consists of (a) motivational workshops and (b) implementation of social-cognitive group counselling techniques in the classrooms. The "light" intervention includes only group counselling activities.

1.Workshops

In the motivational workshops students are guided to identify their personal strengths and career aspirations. The presenters tell stories from their own lives with the aim of promoting self-exploration among students. The presenters participate in group works and help guide the discussions. The development of the group counseling techniques is based on earlier experience in group methods aiming at career choice preparedness (Vuori et al. 2008).

2. Group counselling

Student counsellors from each intervention school participate in two-day teacher training organized by the Finnish Institute of Occupational Health. During the training counselors are familiarized with the content and counselling principles of the intervention. After the training workshop, the counselling techniques are implemented at school. Implementation support will be provided throughout the year to schools. In addition, school counselors will receive structured guidelines and learning material to help carrying out the intervention. Group counselling techniques comprise four components:

a. Career management skills training. The students are supported to identify personal strengths and career interests, explore various occupational choices and set meaningful career goals and plans.

b. Active teaching and learning methods. Instead of lecturing, the trainers use the knowledge and experiences of the participants themselves as part of the learning process. Teachers activate and facilitate the learning process and guide the participants towards the desired conclusions.

c. Supportive learning environment. Principles of social learning (Bandura 1986) guide the student-centered approach, which includes mastery experiences through problemsolving exercises, learning vicariously and receiving peer reinforcement during group discussions.

d. Inoculation against setbacks (Meichenbaum 2017). Students are encouraged to analyze obstacles and setbacks they may face in their educational and occupational careers. The students are guided in problem-solving processes where they learn to cope with the stress related to education and career transitions. The purpose of inoculation against setbacks is self-preparation for coping with problems in career and school transitions due to lack of social support and guidance, conflicts with friends or parents and lack of confidence in one’s own success.

Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ 1986.

Meichenbaum, D. (2017). Stress inoculation training: A preventative and treatment approach. In The Evolution of Cognitive Behavior Therapy, pp. 117–140. Routledge.

Vuori, J., P. Koivisto, P. Mutanen, M. Jokisaari, and K. Salmela-Aro (2008). Towards working life: Effects of an intervention on mental health and transition to post-basic education. Journal of Vocational Behavior 72(1), 67–80.

2016-08-11

2020-05-30

1. Graduation from upper-secondary education
2. Enrollment in tertiary education

1. We use data from the Registry of educational degrees to examine first the effect of the intervention on the probability of graduating from secondary education three years after the intervention. We will then subsequently examine the effect of the intervention on graduating from secondary school four, five, six and seven years since leaving mandatory education as we gather more data. We measure this outcome by using a binary variable for having graduated from any upper-secondary program. In addition, we will create a measure of the quality of the secondary education using data on earlier graduates.

2. We examine the effect of the intervention on enrolling in tertiary education four years after the intervention and subsequently five, six and seven years later. Enrollment will be measured with a binary variable that takes value one if the individual is enrolled in any kind of tertiary education. As above, we will also create a measure of the quality of tertiary education using data on earlier graduates.

1. Application behavior of the subjects
2. Enrollment in upper-secondary education during the two years after graduation.
3. Enrollment in any education during the two years after graduation
4. Employment and earnings
5. Program switches and grades in upper secondary education
6. Social transfers
7. Criminal activity

1.Application behavior of the subjects:
We use data from the applications registers to examine the effect of the intervention on the application portfolio of the subjects. Since there are several ways of characterizing application portfolios, we limit our choice of outcome variables by following (Goux et al. 2017) and examine the effect of the intervention on:
a. the probability of applying to post-mandatory education at all
b. the probability that the application portfolio contains at least one vocational programme
c. the probability that a vocational programme is ranked first
d. the probability that a vocational programme is not ranked first
e. the portfolio only contains academic programmes.
Furthermore, we will characterize the secondary school programmes by calculating the predicted probabilities of graduating from each programme for all the individuals given their compulsory school grades and demographic characteristics. These probabilities are derived from regressions where graduation in each programme is regressed on school grades and demographics using preintervention data.

2.Enrollment in upper-secondary education during the two years after graduation:
We will use a binary variable for having been enrolled in any school during two years following graduation (e.g. for those graduating in spring 2017, the follow-up period will be from the fall term of 2017 to the fall term of 2019). We will also use a measure of the type of program the students are enrolled in. The likely alternatives in this regard include splitting the programs into preparatory studies, vocational track and academic track and/or using finer summary measures of the program type such as average outcomes of previous students enrolled in the school and/or program.

3. Enrollment in any education during the two years after graduation: This includes upper-secondary education as well as the supplementary classes of the comprehensive school.

4.Employment and earnings. We obtain data on annual earnings and months of employment for all the years following the intervention. — We create an indicator for being neither employed or enrolled (NEET). We will examine the effect of the intervention NEET.

5. Program switches and grades in upper secondary education.

6. Social transfers: Use of social transfers that are identifiable in the register data during ages 18-25.

7.Criminal activity: We use data on decisions by the district courts to examine the effect of the intervention on the propensity to commit a crime that leads to a conviction in a district court during ages 18-25. In addition, we will create a measure of the seriousness of the offences using data on previous cohorts. For this measure we will combine data from the district courts with data on offences and coercive measures registered by the police. We will specify this measure in more detail in a later update to this PAP.

Target schools

Our target population consists of Finnish-speaking middle schools with high dropout rates located in 16 municipalities (Espoo, Helsinki, Hyvinkää, Hämeenlinna, Jyväskylä, Järvenpää, Kirkkonummi, Kotka, Lahti, Lappeenranta, Oulu, Porvoo, Salo, Tampere, Turku, Vantaa). We exclude speciality schools such as Steiner schools, schools exclusive targeted to disabled students and schools that do not have at least two parallel classes for grades 7-9. In total, our target population consisted of 92 schools that had 480 regular classes and approximately 8,400 ninth grade students.

School-level randomization

We used randomized block design to divide schools into treatment and control groups. We created stratas using school-level dropout rates constructed with data from Statistics Finland's Sijoittumispalvelu. "Dropouts'' were defined as individuals who had not obtained a secondary degree and were not enrolled in any school four years after graduation. Within each municipality, we ranked the schools according to their earlier dropout rates and divided them into bins of two schools. The randomization for the first two rounds were conducted simultaneously. For these rounds, we also randomized the bins into those treated during the 2016--2017 and 2017--2018 academic years. The motivation for this step was to ensure that we do not create correlation between the year the school was treated and school "quality''. From each bin, we then randomized one school to become a treatment school, while the other one would become a control school. The school-level randomization of the remaining two rounds were conducted during the spring term of the academic year preceding the intervention.

Within-school randomization

Once the schools were recruited to participate, we randomized classes in the treatment schools into intensive and light treatment groups (see above). In the control schools, we randomized classes into a group for which we conducted the same survey as in the treatment schools and into "pure control", which was not contacted at all.

Not available

Randomization done in office by a computer

School-level and subsequently class-level randomization

Yes

92 schools and 480 classes

8,400 pupils

Treatment group consists of 46 schools and 240 classes, with 120 classes receiving intensive treatment (workshops and group counselling) and 120 classes light treatment (only group counselling). Control group consists of 46 schools and 240 classes, with 120 classes participating in survey and 120 assigned to "pure control".

The sample size will allow us to detect effects of 2.4 percentage points change in graduation from upper secondary education four years after graduating from high school for power of 80% with a significance level of 5%. The power calculations are conducted by running simulations using data on students graduating from high schools in the treatment area in 2003–2008.

Oma linja school intervention: Preliminary pre-analysis plan