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Record W4408239935 · doi:10.1080/10901027.2025.2473734

Early childhood education in Cambodia: preschool teachers’ beliefs, curriculum priorities, and professional development needs

2025· article· en· W4408239935 on OpenAlexaff
Shahid Karim, Alfredo Bautista, Norman B. Mendoza, Sok Soth, Kerry Lee

Bibliographic record

VenueJournal of Early Childhood Teacher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCambodian History and Society
Canadian institutionsRoyal Ottawa Mental Health Centre
FundersWorld Bank Group
KeywordsPsychologyEarly childhood educationEarly childhoodProfessional developmentCurriculumPreschool educationPedagogyEarly childhood teacherFaculty developmentTeacher educationMedical educationDevelopmental psychologyMathematics educationMedicine

Abstract

fetched live from OpenAlex

Improving the quality of preschool education is a key goal of curriculum reform initiatives worldwide, including in developing countries such as Cambodia. The preschool curriculum in Cambodia focuses on five core learning areas: mathematics, science, social science, Khmer literacy, and psychomotor skills. This study explored Cambodian preschool teachers’ beliefs about how children learn, their curriculum priorities, and professional development (PD) needs. The study recruited 409 teachers from the three types of preschools in the country. Data were collected through an online survey. We found that participants held child-centered teaching beliefs and considered science and social science the most important learning areas for children. They reported high PD needs in mathematics and Khmer literacy. No differences were found when comparing participants’ responses across educational level, teaching qualification, age, and teaching experience. The study findings offer valuable insights into preschool teachers’ teaching beliefs, curriculum priorities, and PD needs, which may inform teacher education policy and practice in Cambodia and other 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.275
Teacher spread0.269 · 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
Published2025
Admission routes1
Has abstractyes

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