Challenges and Strategies for Enhancing Quality Nigeria Certificate in Education (Technical) Programme in Kaduna State, Nigeria
Bibliographic record
Abstract
The study was designed to identify challenges and determine strategies for enhancing the quality of Nigeria Certificate in Education (Technical) programme in Kaduna State. The study adopted a descriptive survey research design. Three research questions were raised for the study, while three null hypotheses were formulated and tested at 0.05 level of significance. A structured questionnaire was used to collect data from 103 respondents (19 management staff and 84 lecturers) in the study area. The questionnaire was validated by three experts and the reliability coefficient was established to be 0.88 using Cronbachs Alpha method. Data collected were analysed using mean and standard deviation and z-test was used to test the hypotheses. The study examined two challenges that posed as a threat to quality NCE (Technical) programme, namely; student, government-related challenges. The study revealed that adequate funding; training and retraining of teachers; provision of required infrastructures and facilities; adequate internal and external supervision among others are potential strategies for enhancing quality NCE (Technical) programmes in Kaduna State . Based on the findings, the study recommended that in-service training and retraining should be made available and frequent for teachers. National Commission for Colleges of Education should make sure they live up to expectation to ensure sustenance of effective quality assurance mechanisms in all the institutions in order to achieve the goals of technology education in Nigeria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".