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Record W29886359 · doi:10.5206/cie-eci.v42i2.9228

Empowering Teachers to become Change Agents through the Science Education In-Service Teacher Training Project in Zimbabwe

2013· article· en· W29886359 on OpenAlexaffvenue
Yovita Gwekwerere, Emmanuel Mushayikwa, Viola Manokore

Bibliographic record

VenueComparative and International Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsNorQuest CollegeLaurentian University
Fundersnot available
KeywordsCurriculumTeacher educationScience educationPedagogyTraining (meteorology)Mathematics educationProcess (computing)Medical educationSociologyPsychologyMedicineComputer scienceGeography

Abstract

fetched live from OpenAlex

This paper presents findings from a study of three Zimbabwean science teachers who participated in the Science Education In-service Teacher Training (SEITT) program. At the turn of the century, the SEITT program was designed to develop science and mathematics teachers into expert masters and resource teachers for Zimbabwe’s ten school districts. The study investigated the successes and challenges faced by the three teachers who were in the process of reforming their pedagogical practices as well as writing and using contextualized science curriculum materials to teach secondary science. Data were collected through telephone interviews. The three teachers reported that the SEITT program helped them to transform their practice as well as that of their peers. They also reported that changing their teaching methods motivated learners to actively participate and this change also resulted in improved teacher efficacy. The paper discusses implications for improving science teaching and suggestions for contextualizing the science curriculum in developing countries.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.214
GPT teacher head0.493
Teacher spread0.279 · 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 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

Citations8
Published2013
Admission routes2
Has abstractyes

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