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Record W1932642182 · doi:10.21083/ajote.v3i1.1974

Determinants of Preschool Teachers' Attitudes towards Teaching

2013· article· en· W1932642182 on OpenAlexvenueno aff
Florence Njeri Kinuthia, D. K. Kombo, Maureen Mweru

Bibliographic record

VenueAfrican Journal of Teacher Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Professional Development and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsChristian ministrySimple random sampleMedical educationNonprobability samplingPsychologyMathematics educationSchool teachersQualitative propertyPedagogyMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

This study sought to investigate determinants of pre-school teachers’ attitudes towards teaching in Thika Municipality, Kenya. The concern to stakeholders was the negative attitudes of the pre-school teachers towards teaching. Such a concern called for investigation. To accomplish this task, a descriptive survey design and Ex-post facto design were used. A total of 53 pre-school teachers and 12 administrators participated in the study. A simple random technique and purposive sampling were employed to identify study samples. In addition, questionnaires and interviews were used to collect data. The statistical procedures were carried out using the statistical package for social sciences (SPSS). Qualitative and quantitative techniques were employed in order to analyse the obtained data. The study revealed there were still low levels of training among pre-school teachers, teachers with few years of teaching experience were the ones involved in teaching in the pre-schools and teachers in public pre-schools were more positive towards their job than their colleagues in private schools. Among the recommendations were that pre-school teachers work under the Ministry of Education and an attempt be made to improve the retention level of teachers. Administrators should also work on modalities of motivating their teachers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.534

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 studies0.0000.000
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 head0.027
GPT teacher head0.358
Teacher spread0.332 · 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 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".

Quick stats

Citations3
Published2013
Admission routes1
Has abstractyes

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