Assessing Teaching Readiness of University Students in Cross River State, Nigeria: Implications for Managing Teacher Education Reforms
Bibliographic record
Abstract
This ex-post-facto designed study was geared towards assessing the readiness of would-be teachers in universities inCross River State for the teaching profession, and how reforms can be managed to strengthen this. Three hypotheseswere isolated to give direction to this investigation. 200 students from the two universities in the state constituted thesample drawn from a population of 1684 graduating education students. Data were generated using “Students’Teaching Readiness Questionnaire (S.T.R.Q.)”. Population t-test and Independent t-test statistical techniques wereused to analyze data collected. Results disclosed that teaching readiness of university education students issignificantly low in terms of possession of communication skills, interpersonal skills, ICT skills and entrepreneurialskills; gender influences teaching readiness of university education students in one hand and in the other, it does not;teaching readiness of university education students does not significantly differ on the basis of institution ofaffiliation. On the strength of these findings, implications for managing teacher education reforms were articulated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".