All time references are in CEST
How can we measure post-school educational contexts? – Analysing the interplay between students, schools, firms and universities |
|
| Session Organisers | Dr Katarina Wessling (Maastricht University ) Dr Dominik Becker (Federal Institute for Vocational Education and Training (BIBB), Bonn) |
| Time | Tuesday 18 July, 09:00 - 10:30 |
| Room | |
After completing general secondary school, students can – depending on their national educational system – enter different forms of vocational education and training (VET) or higher education (HE). These forms of post-school education occur in various contexts such as vocational schools, training firms, colleges, universities of applied sciences or research universities. In countries that offer on-the-job VET programs, dual training or dual studies, students are trained jointly in firms and vocational schools or universities. To gain a comprehensive understanding of how the learning contexts influence individuals' educational success, skill acquisition, and labour-market outcomes, it is essential to gather data on these contexts. Specifically, linked data that integrates information about individuals, schools, firms, and universities is crucial for a more holistic analysis.
In this session, we discuss data and substantive research on the measurement and analysis of post-school contexts in the above sense, and their impact on students’ educational and employment outcomes.
We are interested in the following research topics:
Data sources: Discussing newly available data sources (survey, register, institutional, curricular, job advertisements etc.) that allow for analysing the contexts of post-school education
Data linkage: Linking firm-level and vocational school-level, college-level and/or university-level data with student/apprentice-level data
Measurement of context conditions in vocational schools, higher education institutions and firms:
- Expectations (and effectiveness) of VET schools teachers, HE educators or firm instructors
- Instruction quality in VET schools, HE institutions or on the firm level
- Social, gender, skill, or other forms of composition in VET schools, HE institutions or firms
Analyses of effects of context conditions in vocational schools, higher education institutions and firms: Effects on objective and subjective labour-market outcomes, e.g. skill acquisition, wages, employment prospects, mismatch or occupational mobility, vocational interests, work values, regrets in career choices
Keywords: data linkage, data harmonisation, vocational schools, universities, firm data
Ms Nele Theuer (Federal Institute for Vocational Education and Training) - Presenting Author
Dropout from vocational education and training (VET) is associated with long-term career disadvantages, such as unemployment (Deuer, 2003). One relevant predictor, which has been shown to lower dropout risk, is satisfaction with VET (Holtmann & Solga, 2023).
In our study, we add to research on the satisfaction-dropout-relationship by taking a contextual perspective: Specifically, we draw on peer group effect research and social contagion research, which argue that peers’ beliefs, behaviours or affect spill over to the individual (Burgess et al., 2018; Ryan, 2000). Accordingly, we suggest that dropout from VET is not only affected by individual but also contextual – i.e. group average - satisfaction.
Indeed, studies show that average classroom/school satisfaction is positively associated with subsequent individual satisfaction (King & Datu, 2017; Nikolov & Dumont, 2020). Furthermore, both individual and group average satisfaction at the workplace predict voluntary turnover (Wang et al., 2016). Hence, we hypothesize that there is a) a direct effect of peer group satisfaction and b) an indirect effect (i.e. mediated by subsequent individual satisfaction) on dropout from VET.
We work with data from Starting Cohort 4 of the National Educational Panel Study (NEPS-SC4, Blossfeld & Roßbach, 2018). Our sample consists of 2,257 adolescents who reported to be in VET during two time points, one year apart. We measured the predictor variables, individual and peer group satisfaction with VET, at time point 1. To account for peer group satisfaction, we averaged individual scores of all VET students within occupational groups, which encapsulate peer influences from both learning environments in German VET: VET schools and training firms. The mediator variable, subsequent individual satisfaction, was measured one year later. Dropout from VET was measured using self-reported episodic data. We aim to conduct multilevel structural equation models to test our hypotheses.
Dr Dominik Becker (Federal Institute for Vocational Education and Training) - Presenting Author
Mrs Myriam Baim (Federal Institute for Vocational Education and Training)
Mrs Sandra Müller (Federal Institute for Vocational Education and Training)
Professor Harald Pfeifer (Federal Institute for Vocational Education and Training)
Mrs Marion Thiele (Federal Institute for Vocational Education and Training)
Ms Nele Tschöpe (Federal Institute for Vocational Education and Training)
When time-dependent contextual data shall be merged to individual-level panel data, a challenge arises when the temporal units of both data sources differ. Suppose an individual-level outcome is measured by asking respondents about its occurrence since the previous survey wave, but the outcome shall be predicted using contextual data collected on a monthly or quarterly basis. In such cases, researchers can either make an informed guess about the outcome’s typical occurrence in a specific month/quarter – or use a predictive model incorporating external data.
In our applied example, we aim to estimate business-cycle effects (BCEs) on individuals’ participation in job-related non-formal further training (FT), measured across 13 waves of Starting Cohort 6 of the German National Educational Panel Study (NEPS-SC6). Respondents were asked whether they had participated in job-related non-formal FT since their last survey wave. To predict this outcome, we use monthly and quarterly business-cycle information, i.e., unemployment rates and federal gross-domestic product.
To address the issue that the exact month of FT participation is unspecified within NEPS, we employ Rubin’s (1986) multiple-imputation (MI) based statistical-matching approach of information from donor to recipient data (Alpman 2016). As donor data, we use three waves of the German Socio-Economic Panel Study (GSOEP), where FT participation has been measured, albeit only occasionally, with precise timing. Using an empirically-derived predictive model of FT timing, we estimate FT months for specific groups of individuals and assign these to their statistical twins within NEPS. This predicted timing of FT is then used to align monthly and quarterly business-cycle information with the NEPS data. Particular emphasis will be placed on the impact of different prediction models used in the matching process on the estimated BCEs on FT.
Professor Barbara Worek (Jagiellonian University) - Presenting Author
Professor Jolanta Perek-Białas (Jagiellonian University)
This presentation outlines the comprehensive approach to studying adult educational activity in Poland, as developed within the Human Capital Balance (HCS) project, conducted by Jagiellonian University in partnership with the Polish Agency for Enterprise Development since 2008. Based on large, representative samples—approximately 4,000 individuals, 3,500 employers, and 1,000 training providers per wave—the project provides robust insights into the contexts and dynamics of post-school education.
Our research investigates diverse forms of learning, encompassing formal education, non-formal training, and informal learning, with a focus on how post-school contexts—such as vocational schools, training firms, and higher education institutions—shape educational and labor market outcomes. We analyze factors such as education levels, employment status, age, and health, and explore relationships between competency development and outcomes including self-assessed skills, job satisfaction, and occupational mobility. Employer strategies for workforce development and their effectiveness, as well as the role of public and private training providers, are also key areas of focus.
The HCS study employs a novel methodological framework, integrating diverse data sources, including survey and institutional data, while addressing significant challenges in linking information across stakeholders. The presentation will discuss how these challenges were overcome, highlight findings on the effects of learning contexts, and provide insights into how competency development data can support lifelong learning systems. Key questions include: What factors shape adult educational engagement in Poland? How do post-school contexts influence skill acquisition and labor market outcomes? What methodological challenges arise in integrating multi-level data, and how can these insights be applied to improve policy and practice?