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Accommodating the plurality of voices:A qualitative study exploring teachers’ challenges and needs in the institutionalization of public engagement in higher education

2023· article· en· W7054454019 sur OpenAlexaff

Notice bibliographique

RevueDigital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) · 2023
Typearticle
Langueen
DomaineEngineering
ThématiqueLaser Design and Applications
Établissements canadiensAthena Sustainable Materials Institute
Organismes subventionnairesnon disponible
Mots-clésInstitutionalisationPublic engagementHigher educationPopularityCurriculumCommunity engagementQualitative researchCivic engagementBest practice
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In recent years, community service learning (CSL) has gained popularity as a form of public engagement in universities and higher education institutions (HEIs) worldwide (1,2). The positive impacts of CSL are being recognized and embraced, so the current challenge is no longer to only improve and expand these efforts, but also to institutionalize CSL in a meaningful way for all stakeholders involved (1,3–5). Achieving this requires both top-down and bottom-up strategies to embed public engagement in the institution’s mission and policies and to reflect it in daily activities to become part of a university’s culture (3,6–8). Teachers, as critical components of the educational system in HEIs, play a vital role in shaping students’ educational experiences and can provide key insights into effective ways of integrating public engagement into the curriculum (8–11). Encouraging teachers in their engaged educational efforts can help foster a culture of public engagement within the institution, as they can act as advocates for its implementation and promote it among colleagues. However, the challenges that teachers face in integrating public engagement into their teaching practice and institutional structures are diverse and not well understood, and strategies to further support and promote these efforts in this phase of the institutionalization process remain unclear. To address these gaps, this qualitative study aimed to gain a more in-depth understanding of teachers’ challenges and needs in their engaged educational practices and identify the strategies and actions needed to further thrive the institutionalization process. This can help HEIs to better understand how to institutionalize public engagement efforts in a way that is sustainable and responsive to the needs of teachers. To contribute to this, we performed 25 qualitative semi-structured interviews with teachers at the Vrije Universiteit (VU) Amsterdam who implemented CSL in their courses. Purposive sampling was used to recruit a diverse group of participants, including teachers from different faculties, using various formats of CSL, and with varying degrees of experience in implementing CSL in their courses. From our analysis, we identified three major needs of teachers. Firstly, they desire an internal community or network to exchange experiences and get inspired. Secondly, teachers indicated the need for resources, such as standardized tools (e.g., rubrics and assessment forms), training and support (e.g., training in the code of conduct wen working with societal partners), but also matching moments where they can meet societal partners. Finally, teachers expressed a need for enhancing both internal and external promotion of public engagement efforts. Additionally, teachers stressed that more time and budget for the design, implementation, monitoring, and evaluation of courses are needed. Including public engagement efforts in the academic reward system, alongside academic publications, was also mentioned as crucial for the continuation of their practices in the long run. In conclusion, this study highlights the challenges and needs of teachers in integrating public engagement into their teaching practices and institutional structures. By implementing policies and strategies that are responsive to teachers’ needs, HEIs can foster a culture of public engagement that aligns with their mission.

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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,122
Score d'incertitude au seuil0,557

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
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 Opus0,144
Tête enseignante GPT0,288
Écart entre enseignants0,144 · 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'étudeQualitatif
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

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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