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Assessing Professionalism in Teaching: The Secondary Education Perspective in Cross River State, Nigeria

2014· article· en· W1767013422 on OpenAlexvenueno aff
CO Ukpor, N. I. Ashibi, SG Akpan, Abigail Edem Okon

Bibliographic record

VenueHigher education of social science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Education and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsStratified samplingEnforcementTest (biology)Perspective (graphical)Mathematics educationPsychologyMedical educationSample (material)State (computer science)MedicinePolitical scienceMathematicsStatisticsLaw

Abstract

fetched live from OpenAlex

The study examined whether or not teaching is a full profession. It also determined the relationship between professionalism in teaching and teaching effectiveness at the secondary education level in Cross River State, Nigeria. A sample of 850 educators (844 teachers, 3 staff of Teachers’ Registration Council of Nigeria and 3 heads of inspectors of schools) was selected through stratified random sampling, judgemental and wholistic techniques respectively. A 20-item researcher-made questionnaire was used to collect data from respondents. Survey design was adopted. Test statistics adopted for data analysis were frequency, weighted mean and standard deviation. A mean score of 2.00 and above formed the significant/acceptance level. It was found that teaching is a profession but not in its fullest sense, and that there is a strong and positive relationship between professionalism in teaching and teaching effectiveness in the study area. It was recommended that licensing should be an essential pre-requisite for entry into teaching; a uniform and lengthy training period should be maintained in all teacher training institutions and be followed by inductive training. There should be strict enforcement of Education Act 31 of 1993; and more awareness be created among teachers that professionalism in teaching is essential in their career and depends partly on them.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
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 head0.033
GPT teacher head0.449
Teacher spread0.416 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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