Dienst Uitvoering Onderwijs

We propose a dynamic factor model which we use to analyze the relationship between education participation and national unemployment, as well as to forecast the number of students across the many different types of education. By clustering the factor loadings associated with the dynamic macroeconomic factor, we can measure to what extent the different types of education exhibit similarities in their relationship with macroeconomic cycles. To utilize the feature that unemployment data is available for a longer time period than our detailed education panel data, we propose a two-step procedure. First, we consider a score-driven model which filters the conditional expectation of the unemployment rate. Second, we consider a multivariate model in which we regress the number of students on the dynamic macroeconomic factor, and we further apply the k-means method to estimate the clustered loading matrix. In a Monte Carlo study, we analyze the performance of the proposed procedure in its ability to accurately capture clusters and preserve or enhance forecasting accuracy. For a high-dimensional, nation-wide data set from the Netherlands, we empirically investigate the impact of the rate of unemployment on choices in education over time. Our analysis confirms that the number of students in part-time education covaries more strongly with unemployment than those in full-time education.

Virtual Reality technologies have been established as an effective tool for the treatment of a variety of mental health disorders. Despite those positive results, it remains unclear whether these findings can generalize to the healthy population. In the current study, we assessed to what degree a virtual scenario consisting of a tropical beach can be successfully applied for the reduction of state anxiety and negative mood in healthy individuals. The study was performed on 50 volunteers aged 18 to 45 that were individually exposed to virtual reality. Before and after the virtual reality session, the emotional state of the participants was measured through questionnaires. Using linear mixed effect models, we obtained evidence for the effectivity of a single virtual reality session for negative state reduction, namely, we observed a decrease in state anxiety and negative mood. Additionally, a positive mood of participants increased after the session, however, no statistical evidence was obtained. The results confirm our hypothesis that state anxiety and negative mood can be decreased using a low-cost virtual reality device. Based on our findings, we suggest that virtual scenarios can improve the emotional well-being and the quality of life of the general population.

Paper based diplomas are easy to forge and require a noticeable amount of expertise to verify, especially when presented in a different country from the one that produced them. The so-called diploma mills, illegal operations that sell fake degrees, are running rampant at the moment. Moving to fully digital diplomas can prevent these problems. Digital identity technologies can be used for providing controlled access to verifiable information about an individual academic achievement. The Groningen Declaration has become the centre of a world wide effort to join efforts dedicated to the creation of Digital Diploma Deposits, encompassing developments from China through Mexico and Norway through South Africa. At the core of this efforts lays the work of groups like EUNIS's own RS3G (Rome Students Systems and Standards Group), EAIE's DSDP (Digital Student Data Portability), USA National Student Clearing House or TERENA TF-EMC2. The aim of the present paper and the corresponding presentation is to increase awareness in the European University ICT community of the efforts that surround the Declaration, by presenting the achievements of the first year of its existence, like the meeting that took place in April 2013 in Beijing hosted by CHESSIC and the Chinese MoE.

We present ongoing developments and projects concerning the digitization of Higher Education Use Cases, Processes and Infrastructures in the context of EU developments and standardization efforts (EU SDG). These use cases and projects include: (i) student applications at Higher Education Institution (HEI) portals at HEI and at the Stiftung für Hochschulzulassung (SfH) sites for admissions; (ii) secure and trustworthy production of exam certificates for HEI and EDU applications; (iii) student mobility digitization, e.g. ELMO, EMREX, EDCI, eIDAS, PIM, and (iv) standardization efforts embedded in the EU contexts such as XHigherEducationInstitutionExchange (XHEIE). Orchestrating the process participants (SfH, universities, applicants) is necessary for effective operation and calls for the strategic and systematic expansion of digitization. In the future the Online Access Act (OZG) reinforces the need to digitize admissions processes operated by the participants.

EMREX is a solution for transferring student data internationally in a machine- readable way and in a structured format. The EMREX service network has been in production since 2017. ELMO is the data standard used in the EMREX network to describe student achievements and supporting data. One of the strategic goals established for EMREX in 2022 by the Executive Committee of the EMREX User Group was to support one or more wallet solutions. For the EMREX-used data standard ELMO a strategic goal was set to keep ELMO compatible, as far as possible, with other European educational standards: European Learning Model (ELM) and Single Digital Gateway (SDG). The new digital European landscape was shaped by two EU-legislations, bringing into live the European Digital Identity Wallet (EUDI) and Single Digital Gateway, a platform which facilitates online access to information, administrative procedures, and assistance services that EU citizens and businesses may need in another EU country. To fulfil the established strategic goals, some partners from the EMREX community joined the Digital Credentials for Europe (DC4EU) project, consisting of 80 partners from 23 countries. The aim is to create a working pilot installation of a digital wallet that conforms to the EUDI wallet framework. The participation is expected to add value both to the EMREX-community and other organisations handling or in need of educational data. This paper brings all these initiatives into common perspective.

We propose a dynamic factor model which we use to analyze the relationship between education participation and national unemployment, as well as to forecast the number of students across the many different types of education. By clustering the factor loadings associated with the dynamic macroeconomic factor, we can measure to what extent the different types of education exhibit similarities in their relationship with macroeconomic cycles. To utilize the feature that unemployment data is available for a longer time period than our detailed education panel data, we propose a two-step procedure. First, we consider a score-driven model which filters the conditional expectation of the unemployment rate. Second, we consider a multivariate model in which we regress the number of students on the dynamic macroeconomic factor, and we further apply the k-means method to estimate the clustered loading matrix. In a Monte Carlo study, we analyze the performance of the proposed procedure in its ability to accurately capture clusters and preserve or enhance forecasting accuracy. For a high-dimensional, nation-wide data set from the Netherlands, we empirically investigate the impact of the rate of unemployment on choices in education over time. Our analysis confirms that the number of students in part-time education covaries more strongly with unemployment than those in full-time education.

Abstract Dutch secondary education is a multi‐tiered system, and many students are placed in a single track in the first year (seventh grade) of secondary education. As part of the placement procedure, all sixth‐grade students take an end of primary school test. Although these standardised attainment tests (SATs) are considered high‐stakes tests, the predictive accuracy of many of these SATs has not been studied comprehensively. In the current study, three SATs commonly used in The Netherlands (CET, IEP and R8) were compared on their predictive accuracy of educational attainment in ninth grade. The predictive accuracy was also investigated for particular track recommendations, using multiple cohorts of population data, each of around 160,000 students. For the two most recent cohorts, CET was overall a more accurate predictor of educational attainment in ninth grade than R8, which in turn was a more accurate predictor than IEP. All three attainment tests had only mediocre to acceptable predictive power overall. Furthermore, all three tests had very poor predictive power for certain single‐track recommendations. For these track recommendations, many students attained a lower or higher educational level in ninth grade. This study is the first to compare the predictive accuracy of these SATs. While statistical noise is normal when using tests, since different tests are used that vary widely in predictive accuracy, there is much more noise than is acceptable. As long as the predictive accuracy is poor to mediocre, these SATs should not have a large role to play in the Dutch placement procedure for secondary education.