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Enregistrement W7019001079

Faculty development units at Mexican higher education institutions: A descriptive study of characteristics, common practices and challenges

2011· article· en· W7019001079 sur OpenAlexaboutno aff

Notice bibliographique

RevuePurdue e-Pubs (Purdue University System) · 2011
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEvaluation of Teaching Practices
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHigher educationInstitutionVariety (cybernetics)Faculty developmentWork (physics)Descriptive researchDescriptive statisticsProfessional development
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The rapid expansion in higher education in the 1960s and early 1970s brought a reexamination of university teaching and learning, placing significant attention on the role of faculty development. The steady growth of this field has been reflected in the establishment of centers, offices, and divisions at many colleges and universities that are in charge of the design, implementation, and evaluation of faculty development programs. Reflecting different traditions within institutions and between countries, these special units exist under a variety of names (e.g., teaching and learning centers, educational development units, institutes for academic excellence). Starting in the 1960s, several nationwide empirical studies have been conducted to characterize the practices of these units in the United States and, to a lesser extent, in other countries such as the United Kingdom, Canada, Australia, South Africa, and Mexico. In order to provide up-to-date information about the work of faculty development units (FDUs) to different Mexican stakeholders (e.g., faculty developers, institution administrators, and policymakers), this study targeted the leaders of FDUs at 220 higher education institutions affiliated with the Mexican National Association of Engineering Schools (ANFEI). A pragmatist worldview guided the design of the two-phase study. In phase I, FDU leaders’ contact information was collected through an analysis of institutions’ websites and a survey of institutions’ ANFEI representatives. Information about the FDUs was gathered in phase II through a Web questionnaire that explored FDU leaders’ profiles; FDUs’ goals, services, evaluation practices, influences and challenges; and potential new directions for the field of faculty development in Mexico. The instrument was developed based on a questionnaire identified in the literature that was translated into Spanish and adapted to the Mexican context with the help of three experienced Mexican faculty developers. The survey yielded 47 suitable responses from a variety of institution types and geographic regions across Mexico. Participants’ responses reveal that faculty development practices in Mexico are highly influenced by federal policies aimed at improving the academic profile of Mexican faculty. Salient responses also indicate that Mexican FDUs face several challenges such as providing adequate discipline-based and teaching development opportunities for faculty; providing support to faculty on the diverse roles they have to meet; guaranteeing that the faculty development programs are relevant and at the forefront of educational innovation; contributing to the overall academic quality of higher education institutions; and securing sufficient funding and resources. Results from this study suggest that faculty development as a field and as a profession in Mexico is still emerging. Mexican faculty development leaders could benefit from the lessons learned by a number of international faculty development organizations that have arisen in the last decades and from the expertise they share through specialized venues such as journals and conferences. As engineering education is also emerging as a recognized scholarly field in Mexico, there is a potential for establishing strategic partnerships between these two groups of professionals to catalyze the development and diffusion of educational innovations in Mexican engineering schools.

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)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
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 enseignants0,334
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,000
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 Opus0,489
Tête enseignante GPT0,390
Écart entre enseignants0,100 · 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'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
Publié2011
Routes d'admission1
Résumé présentoui

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