Assessing Professionalism in Teaching: The Secondary Education Perspective in Cross River State, Nigeria
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".