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Enregistrement W7163784937

Eine Untersuchung schulischer und beruflicher Übergangsentscheidungen

2021· dissertation· en· W7163784937 sur OpenAlexaboutno aff
Katja Seidel

Notice bibliographique

RevueOpen MIND · 2021
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueEducation Methods and Technologies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGermanNexus (standard)Empirical researchJob satisfactionQuarter (Canadian coin)Foreign languageLeisure time
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This dissertation includes an introduction and five empirical papers focusing on the educational and career decision-making process of individuals in Germany. The five papers embrace different determinants of educational and career decisions including school performance, social background, leisure activities as well as professional expectations, and contribute to the existing literature in this research area. Chapter 2 of this dissertation begins by analysing the nexus between students’ time allocation and school performance in terms of grades and satisfaction with their own performance in mathematics, the German language and a first foreign language, as well as overall achievement. This chapter looks at the heterogeneity of three important extracurricular activities: student jobs, sports and participation in music. Moreover, the heterogeneity of each activity is addressed by accounting for different types of the particular activity and differences in the number of years the activity has been pursued. For this purpose, data from the German SOEP, as a representative panel survey of private households and people in Germany, in particular cross-sectional survey data of 3388 students who are about 17 years old and enrolled in a German secondary school, were used. The main findings are that having a job as a student is negatively correlated with school performance, whereas participation in sports and music is positively correlated. However, the results reveal heterogeneity in each activity, especially with respect to intensity. Chapter 3 addresses the concrete post-school decision of school students, in particular whether to study or to enter the German VET system (Vocational Education and Training). It focuses on individual risk preferences and the social background of individuals and how these determinants affect the ultimate decision to enrol in university or to start an apprenticeship given the same level of qualification. For the empirical approach data from the German SOEP were used, in particular information on individuals' educational decisions between 2007 and 2013. The results indicate that (i) individual risk preferences do not have an overall effect on the real transition; (ii) privileged individuals are more likely to take up higher education; and (iii) compared to highly educated parents, parents without an academic background are less likely to guide their children into tertiary education, regardless of how much they support their children with their school work. Chapter 4 deals with the reconsideration of educational decisions in terms of early contract cancellations in VET. In particular, the effects of a second job on the intention to cancel a VET contract early are analysed for apprentices in Germany. For the empirical approach the representative German firm-level study "BIBB Survey Vocational Training from the Trainee's Point of View 2008", conducted by the Federal Institute for Vocational Education and Training (BIBB), is used. The survey contains 5901 apprentices that were interviewed during their second year of apprenticeship (205 schools, 340 classes, and 15 common occupations). Furthermore, it includes the design, procedures, basic conditions, and quality criteria of apprenticeships. The applied probit regressions show a higher intention to quit if apprentices require a secondary job to cover their living costs. In Chapter 5, new data on 191 apprentices from a vocational school, located in a northern German federal state, are used to validate the empirical results of Chapter 4. This chapter presents new insights into secondary-job-related burdens during apprenticeship. Due to limitations in the data, the applied empirical approach in Chapter 4 lacks to analyse how holding multiple jobs increases the intention to leave an apprenticeship early. Therefore, Chapter 5 includes the investigations of burdens related to the second job. The results indicate a lower intention to quit the apprenticeship if an apprentice holds a second job to cover living costs. However, secondary jobs are linked to lower quality of training, which, on the other hand, increases the intention to leave the apprenticeship early. Furthermore, the probability of secondary-job-related burdens increases with the number of working hours. Chapter 6 concludes the thesis by investigating subjective determinants of early contract cancellations in VET. It examines ten questions on what apprentices want to achieve and how unfulfilled expectations affect the intention to leave the apprenticeship early. The findings of this investigation contributes to the existing research on early contract cancellation. The questions considered include information on the performance, personal development, career development and prospects or position in society and their meaning to apprentices. For the research approach, the "BIBB Survey Vocational Training from the Trainee's Point of View 2008" is considered again. The probit and ordered probit regressions applied show significant effects of job characteristics that represent job security. The expectation of being retained after an apprenticeship and the encouragement to consistently train further decrease the intention to leave the apprenticeship early. Furthermore, women appear to be more affected by job security signals than men, but they also sort more often into occupations with lower retention probabilities. Consequently, this result may be an indication of occupational segregation rather than a sign of differences between sexes.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,943
Score d'incertitude au seuil0,986

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0180,001

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.

Tête enseignante Opus0,103
Tête enseignante GPT0,490
Écart entre enseignants0,387 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeAutre devis
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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