Reading to Write: A Strategy for Improving the Writing Performance of Students of English Language: A Case Study of Ogba/Egbema/Ndoni Local Government Area of Rivers State
Bibliographic record
Abstract
This study focuses on Reading to Write as a strategy for improving the writing performance of students of English language in Ogba/Egbema/Ndoni Local Government Area of Rivers State. An intact class was used to investigate reading to write as a way of improving the writing performance of secondary school students. The research focused on a single class, that is, the entire population of the students in SS 2, which is made up of 56 students. This research work has adopted the transactional theory of writing and reading as the theoretical framework. Two types of questionnaires were used because of pre-test and post-test, study four essay topics were used to test the students initial competent in writing skills before we started the training through reading to improve writing. Simple percentage was used for the pre-test and post-test study. Mean and Standard Deviation was used to analyze the student’s achievement test for both pretest and posttest while ANCOVA was used to test the hypothesis at 0.5 level of significant.From the result from pre-test and post-test, it is crystal clear that reading to write as a strategy can help to improve writing performance of the students and majority of the students has confirmed that this method should be apply in their class room in other to improve their writing skills. Recommendations are also provided.Key
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".