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Impact of the evaluation of teaching devices on the professional conceptions of teachers and school administrations in Senegal: the case of five groups of elementary school in Dakar

2022· article· en· W4281640534 on OpenAlexaff
Mamadou Vieux, Lamine Sane

Bibliographic record

VenueBritish Journal of Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCurriculumInclusion (mineral)School teachersDiversity (politics)PerceptionModalitiesWork (physics)Mathematics educationPsychologyProfessional developmentProcess (computing)PedagogyMedical educationSociologyMedicineComputer scienceEngineeringSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this article is to identify characteristics of the work situation that could encourage or increase the commitment of teachers and curriculum is defined according to two modalities characterizing the organization of teaching work in the school facility: cellular versus integrated. A process for evaluating teachers' and school administrations' conceptions is then developed with reference to this organization of school career paths. The empirical study focuses on 5 primary school teams trained for three years in the development and evaluation of their collective arrangements. An analysis of the responses (N == 64) to a questionnaire enabled us to compare the trainees' conceptions with those of other teachers. The results show that the tested system modifies the perceptions of the practitioners in order to better take into account the inputs of the two professional groups (teachers and principals) involved in the curriculum, the diversity of the learners and the inclusion of a personal practice improvement dynamic.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.007
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.140
GPT teacher head0.504
Teacher spread0.364 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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