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Record W7019001079

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

2011· article· en· W7019001079 on OpenAlexaboutno aff

Bibliographic record

VenuePurdue e-Pubs (Purdue University System) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationInstitutionVariety (cybernetics)Faculty developmentWork (physics)Descriptive researchDescriptive statisticsProfessional development
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.489
GPT teacher head0.390
Teacher spread0.100 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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