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Record W3021615182 · doi:10.5539/gjhs.v12n5p153

Teachers’ Perceived Communication Instructional Skill for Improving Teaching in Primary School in Enugu State, Nigeria

2020· article· en· W3021615182 on OpenAlexvenueno aff
Elizabeth N. Ebizie, Obiageli C. Njoku, Juliana N. Ejiofor

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaActive listeningData collectionPsychologyMedical educationInternal consistencySample (material)Descriptive statisticsMathematics educationReliability (semiconductor)Sample size determinationTest (biology)MedicinePsychometricsMathematicsStatisticsDevelopmental psychologyCommunication

Abstract

fetched live from OpenAlex

This study was carried out to investigate teachers’ perceived communication instructional skill for improving teaching in Enugu State, Nigeria. The study was guided by two research questions. The descriptive survey design was adopted for the study. The sample size of the study is 1,196 teachers. Multistage sampling technique was used in the selection of the sample size. The instrument used for data collection was the Teachers’ Perceived Communication Instructional Skills Questionnaire (TPCISQ). To ensure the reliability of the instrument, a trial test was conducted by administering 30 copies of the questionnaire to 30 public primary school teachers in Awka, Anambra State, which is outside the study area. Cronbach Alpha was used to determine the internal consistency of the items. Result of the analysis yielded an overall coefficients value of 0.87. The data collected in the study were analyzed using mean and standard deviation. Based on the findings of the study, it was revealed that teachers need the following communication skills: listening to the pupils attentively when they ask questions; giving proper feedback to the questions raised by the pupils; repeating instructions orally or in writing and so on.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.138
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.372
Teacher spread0.346 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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