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Record W2154447250 · doi:10.5539/ies.v7n13p1

Classroom Management: A Study on the Training Needs of Primary School Teachers

2014· article· en· W2154447250 on OpenAlexvenueno aff
Faida Imhemid Salem El Warfali, Nik Mohd Rahimi Nik Yusoff

Bibliographic record

VenueInternational Education Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsClassroom managementPsychologyMedical educationTraining (meteorology)Data collectionSession (web analytics)School administrationSet (abstract data type)School teachersService (business)Mathematics educationPedagogyMedicineComputer scienceSociology

Abstract

fetched live from OpenAlex

This study aimed to identify the training needs of the in-service primary school teachers in the city of Benghazi, Libya. Data collection involved the administration of a set of questionnaire to 420 teachers and interviews with ten of them. The study found that the most important training needs of the primary school teachers in terms of classroom management were: (i) training on how to improve students’ behavior through the development of codes of conduct for the students at the beginning of the school year, (ii) training on efficient time management to achieve the objectives of the classroom session, and (iii) training on means of modifying the abnormal behavior of the students. Data from the interview revealed some of the methods used by the participants on addressing issues and challenges faced in handling the students. The study supports the importance of organizing classroom management training to in-service primary school teachers to help them manage the classroom effectively.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.085
GPT teacher head0.408
Teacher spread0.323 · 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".

Quick stats

Citations6
Published2014
Admission routes1
Has abstractyes

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