Statistical Reporting in Vocational Education: Review and Ways of Improving
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Notice bibliographique
Résumé
Labor potential for the Ukrainian economy cannot be formed without professional training of staff. The system for professional technical education (PTE) consists of professional technical institutions in an industry, other enterprises, institutions, organizations, and education or supervisory offices charged with the administration of the former. The studies demonstrate that the existing PTE network in Ukraine is ineffective and distanced from the needs of regional economies in terms of their demography problems and needs of their labor markets. The abovementioned raises the importance of the issues of access to high quality and complete statistical information, incorporating a wide range of statistical indicators, first and foremost the ones on labor market performance, enabling for effective decision-making. The author’s review of the respective statistical reports shows that the existing statistical indicators form three linked modules (labor market, formation of PTE system, national accounts of education), containing quantitative data on network, enrolment, teaching personnel, material-technical and methodological provision of professional technical education institutions, PTE financing.
 Sufficiency of the existing statistical information is assessed by use of multi-step typology by the technology based on the statistics of non-numeric data. The data obtained from users and makers of PTE system in time of Turin process in 2016 show that the existing statistical reports fails to meet information needs of labor markets in high quality statistical data. According to the respondents, the main barrier is unstable economic situation; more than one quarter of the respondents (27%) mention irrelevance of the body supervising the collection of statistical data, and lack of advanced methodologies and methods for recording of jobs. A pressing problem is related with overlooking the scopes of shadow jobs and reluctance of a major part of employers to inform the development plans of their enterprises.
 Measures to improve the existing statistical reporting on PTE are as follows:
 
 introduce the questionnaire-based interviews of employers, to calculate the number of graduates kept on jobs, by specialty;
 considering large number of small enterprises and private enterprises, improve the existing method for collection and processing of bid data;
 construct a standard method for calculating the rate of graduates’ job placement using the shadow economy ratio;
 create an integrated information and analytical system for PTE;
 calculate the rate of apprenticeship passed, by specialty, ours of apprenticeship, and location of apprenticeship;
 introduce the monitoring-based assessment of PTE quality;
 develop the method for balancing the scopes of professional technical staff trained in education institutions and labor market needs.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle