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Enregistrement W2972799976 · doi:10.28945/4254

International Curriculum and Conceptual Approaches to Doctoral Programs in Leadership Studies

2019· article· en· W2972799976 sur OpenAlexaboutno aff
Petros G. Malakyan

Notice bibliographique

RevueInternational journal of doctoral studies · 2019
Typearticle
Langueen
DomaineHealth Professions
ThématiqueDoctoral Education Challenges and Solutions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCourseworkEducational leadershipContext (archaeology)Leadership studiesCurriculumExperiential learningTeacher leadershipLeadership stylePedagogyEngineering ethicsSociologyPolitical sciencePublic relationsEngineering

Résumé

récupéré en direct d'OpenAlex

Aim/Purpose: This study explores the various teaching and learning approaches, curriculum design, and program requirements for 70 doctoral programs in leadership. Background: Early research indicates that few studies have addressed learner-centred and process-based approaches to leadership studies among doctoral programs in leadership worldwide. This study is the first complete review of programs in the interdisciplinary field of leadership. Methodology: A qualitative method approach through internet-mediated research was employed to identify explicit and implicit textual data on learning approaches of doctoral programs in leadership. The sample represents a list of 70 doctoral programs in leadership studies and organisational leadership (62 programs are in the United States and eight in Europe, Canada, Philippines, and South Africa). Contribution: This study provides an overview of doctoral program characteristics, delivery methods, coursework and research requirements, discipline-relevant teaching and learning approaches, and process-based approach to leadership. It may serve as a resource and a roadmap to assess teaching and learning approaches of doctoral programs in leadership for program reviews and improvement. Findings: The significant findings of this study are: (a) 91.4% of doctoral programs are coursework-driven, leaving little room for original research. (b) 46% of programs show lack of evidence to context-based approaches to learning (learning as a social activity served outside of classroom environment where learning tools and the context intersect with human interactions). (c) Various teaching and learning approaches, including those prescribed to constructivist, interactionist, situated, and action-based learning approaches. Recommendations for Practitioners: Leadership cannot be understood or learned without social interactions in context. In order to produce experts and “stewards of the field,” a clearer learner-centred strategy to doctoral education, including context-based experiences, should be considered. This pedagogical approach needs to be explicitly articulated (on the public website) to enable students to make an informed decision about doctoral programs in leadership. Recommendation for Researchers: In order to produce theoreticians and “stewards of the discipline” (Golde & Walker, 2006), doctoral curricula design and implementation should seek a balance between coursework, independent research, and creation of collaborative learning environment between students and faculty. Further, due to the shift from the leader-centred to the process-based understanding of leadership, doctoral programs in leadership should consider the relationship process between leaders and followers as one academic inquiry or continuum. Impact on Society: Doctoral programs in leadership that utilise more learner-centred and context-based approaches for knowledge acquisition (epistemologies) as well as studying the leadership phenomenon as a relationship process are more likely to become more impactful and sustainable in society. Future Research: More research seems necessary to identify the extent to which learner-centred approaches within doctoral programs in leadership positively impact on doctoral students’ motivation for learning, program completion, retention, and personal and professional development.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,789
Score d'incertitude au seuil0,730

Scores Codex et Gemma par catégorie

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

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.

Tête enseignante Opus0,818
Tête enseignante GPT0,561
Écart entre enseignants0,257 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations8
Publié2019
Routes d'admission1
Résumé présentoui

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