Theatre-in-Education packages and secondary students’ knowledge of Yoruba orature in Ibadan metropolis
Bibliographic record
Abstract
This study was carried out to determine the effects of two Theatre-in-Education Packages (TiEPs) (Devised-for-students and Devised-by-students) on secondary students’ knowledge of Yoruba orature in the Ibadan metropolis, Nigeria. The moderating effects of Motivation for Yorùbá Orature (MYO) was also examined. The study adopted the Quasi experimental research design of the 3X2 factorial matrix. Three Local Government Areas (LGAs), out of the five existing in the Ibadan metropolis, were randomly selected. The simple random sampling technique was used to select six secondary schools (two from each LGA), while six intact classes of Senior Secondary II students (one per secondary school) were randomly assigned to TiEP Devised-for-students (87), TiEP Devised-by-students (115) and control (90) groups. The instruments used were Yoruba Orature Knowledge Test (r=0.81), Motivation for Yoruba Orature Questionniare (r=0.73) and instructional guides for implementing the TiEPs. Treatment lasted eight weeks. Data were analysed using descriptive statistics and Analysis of covariance at 0.05 level of significance. Results showed that treatment had significant main effect on students’ knowledge of Yoruba orature, in favour of students taught using the TiEP Devised-for-Students. The main effect of MYO and the interaction effect of treatment and MYO were not significant. Therefore, Yoruba language teachers should adopt the principles of TiEP Devised-for-Students in teaching Yoruba orature to their students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".