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Record W4398277776 · doi:10.3389/feduc.2024.1376658

The educational development of university teachers: mapping the landscape

2024· article· en· W4398277776 on OpenAlexafffund
Marilou Bélisle, Valérie Jean, Nicolás Fernández

Bibliographic record

VenueFrontiers in Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversité de MontréalUniversité de Sherbrooke
FundersFonds de Recherche du Québec-Société et Culture
KeywordsDevelopment (topology)Mathematics educationComputer scienceEngineering managementEngineeringPsychologyMathematics

Abstract

fetched live from OpenAlex

This article presents the results of a scoping review designed to explore the current state of knowledge about the educational development of university teachers. More specifically, the study examined the definitions attributed to educational development, its aims, the factors that foster it and the variables studied in this field. A thematic analysis was conducted on 98 scholarly documents published between 2000 and 2022. The results indicate that the field of educational development is mainly characterized by ideological and political rather than scientific dimensions. Consequently, the focus is on desired changes in educational development, reflecting a high degree of desirability. Furthermore, the results highlight the individualistic nature of the starting point of professional learning process, suggesting that institutional conditions and resources should be adapted to accommodate the diversity of learning trajectories. This study contributes to a comprehensive understanding of the complex landscape surrounding the educational development of university teachers, highlighting the need for nuanced approaches to promote teaching quality and professional development in the context of higher education.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.059
GPT teacher head0.366
Teacher spread0.306 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations9
Published2024
Admission routes2
Has abstractyes

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