Learning Doctoral Supervision in Education: A Case Study of On-the-Job Development of Effective Mentoring Practices
Notice bibliographique
Résumé
Aim/Purpose: In this case study research, we aimed to understand the development of effective doctoral supervision practices in Educational Research by examining supervisors’ experiences as doctoral students and how they learned their evolving supervision and mentoring roles as professors. Background: Doctoral supervision is shaped by institutional systems, program structures, research cultures, and national guidelines. Supervisors impact doctoral students’ research experiences, academic success, and personal growth. Many new professors lack formal training, rely on their own experiences being supervised, and learn how to supervise effectively through trial and error and on the job. Methodology: Our case study research involved interviewing five tenured, mid-career doctoral supervisors who were deemed effective based on doctoral student completions. Using reflexive thematic analysis and evaluative coding of interview transcripts, we identified two key findings and nine themes to describe supervisors’ experiences as doctoral students and their on-the-job development and practices as supervisors. Contribution: This study highlights how experiences being supervised as a doctoral student impact and influence the development of supervision practices in combination with various experiences of learning on-the-job during one’s academic career. We expand understanding of the complexity of supervision practice and uncover differences between contemporary contexts and past experiences being supervised. We demonstrate how several supervisors translated impoverished experiences with their own supervisor into targeted efforts to learn how to effectively supervise their own students, to change history, and to deliberately not supervise the way they were supervised. Findings: Two findings are presented: (1) experiences being supervised influence early supervision practices, and (2) learning to supervise on-the-job happens in a variety of ways. Nine themes describe how supervisors’ experiences being supervised influenced their supervisory practices and the various informal on-the-job development approaches, such as learning from students, colleagues, and prior career experiences. Findings highlight the roles of doctoral supervisors, academic peers, doctoral students, programs, and institutions that contribute to developing effective supervisory practices. In our case study, we demonstrate how supervisors can transform academic and research cultures over time. Recommendations for Practitioners: Institutions, programs, and supervisors play crucial roles in ensuring doctoral student success. Institutions should offer structured professional learning and peer mentoring that supports supervisors in developing effective practices early in their careers. By leveraging study findings, institutions can design professional learning opportunities that increase faculty adoption of effective supervision practices and accelerate their learning. Recommendation for Researchers: Given the vital role played by supervisors in research training and talent development of the next generation of researchers and leaders across society, we argue it is crucial to understand and optimize the ways in which doctoral supervisors develop effective supervisory practice as a matter of ongoing research interest. Future research can investigate the importance of intergenerational learning and knowledge transfer in academia, encouraging a more reflective and informed approach to supervisory development. Impact on Society: Findings can inform how to maximize individual, institutional, and governmental investments in higher education. This research can improve outcomes in doctoral education by expanding effective, research-informed development of supervisory practices. Quality supervision impacts doctoral students’ academic success, mental health, and career progression. Understanding supervisory lineage enables universities to enhance current and future doctoral experiences. Future Research: Four questions are provided to guide and promote supervisory development and ongoing research. There is an ongoing need to examine how supervisors and doctoral students define the impact and outcomes of successful supervision and mentoring practices beyond the completion of the thesis.
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 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 | 0,002 | 0,001 |
| 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 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».