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Record W4403989652 · doi:10.6007/ijarbss/v14-i10/22204

Challenges and Quality Evaluation of Preschool Services in Malaysia a Study on the Experiences of Preschool Owners

2024· article· en· W4403989652 on OpenAlexaff
Radhega Ramasamy, Gurdip Kaur Saminder Singh, Ranjit Kaur P. Gernail Singh

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsQuality (philosophy)BusinessPreschool educationPsychologyMedical educationMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

This study analyses the challenges faced by preschool owners in providing quality services and evaluate the research focuses overall quality of services provided by preschool owners, in Malaysia. Parent's naturally like their children to receive a high-quality education in a safe and nurturing environment, but this is not always the case as many parents are faced with problems such as a lack of accurate information. In the methodology part, this study was conducted by quantitative approach. Meanwhile, the instrument of this study is a questionnaire, and the items were adapted from previously validated studies. Data collected through an online survey, and the sample size for this study comprised 300 parents and 114 preschool owners who were chosen via simple random sampling. The random sampling method was used since it produced an unbiased representation of the population and conduct statical analysis. Based on statistical analysis SPSS version 26, the results data was valid and reliable. These research consequences offer some extremely persuasive results that would aid Malaysian preschool facilities in understanding the determinants that motivate parents to send their children to a particular preschool. Additionally, these findings potentially enhance preschool operators develop better services that would meet parents' demands and requirements.

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)0.022
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.401
GPT teacher head0.565
Teacher spread0.165 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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