Text Transformation and Freewriting Methods as Determinants of Achievement in Yoruba Essay Writing in Senior Secondary Schools in Ibadan, Oyo State
Bibliographic record
Abstract
The objective of this study is to determine which of the two teaching strategies: text transformation and free writing could better enhance students’ achievement in Yoruba essay writing. A pretest, posttest, control group quasi experimental design with 3 x 2 x 2 factorial matrix was adopted for the study. A multi-stage sampling procedure was used to select respondent in 1badan north local government area in Ibadan metropolis. Forty students were randomly selected among the SS 2 students in the 3 selected schools. This study has 120 participants in all. Each of the schools was assigned to treatment at random. Data were collected using main instruments: Yoruba essay writing achievement test (YEWAT) and operational guide for instruction in text transformation, free writing and modified lecture methods, (OGITT, OGIFW, OGIMLM). One hypothesis, tested at 0.05 level of significance was formulated for this study. Data were analyzed using analysis of covariance and scheffe post hoc. The findings revealed that there was a significant main effect of treatment on students’ achievement in Yoruba essay writing (f(2,109) = 76.205; p < 0.05). Scheffe post hoc analysis shows that the test transformation method (tt) has the highest mean score of (398.025), free writing (FW) with higher mean score of (388.401) lecture method with high mean score of (319.001). The findings from the study show that text transformation method had a significant impact on students’ writing achievement than other methods. It is recommended that teachers should affect the use of text transformation method in the teaching of Yoruba essay writing in the senior secondary schools while free writing should be used as pre-writing skill to generate content knowledge. Also teachers should use relevant and familiar instructional materials to teach subject matters while parents and students should cultivate positive attitude to Yoruba language as a school subject to protect and preserve our cultural heritage and develop our society morally, socially and technologically.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".