The Influence of Process Approach on English as Second Language Students’ Performances in Essay Writing
Bibliographic record
Abstract
This study examined the influence of Process Approach on English as second language Students’ performances in essay writing. The purpose was to determine how far this current global approach could be of assistance to the writing skill development of these bilingual speakers of English language. The study employed the pre-test post-test control quasi-experimental research design. The sample consisted of 80 senior secondary school final year students. The research material included the senior secondary school English Language recommended textbook, National Examination Council (NECO) and West Africa Examinations Council (WAEC) English Language Syllabi, Federal Ministry of Education English Language Curriculum, English-Language Teachers’ Lesson Notes and Students Essay Writing Exercise books. The West African Examinations Council’s (WAEC) English Language Essay Question as an adapted instrument was used to gather data. The data generated were subjected to statistical analysis and the results of the analysis showed that there was no significant difference between the pre-test scores of both the Control and the Experimental group which indicated the homogenous state of both Control and Experimental groups. There was significant difference in the post-test scores of the Experimental and the Control groups. There was no significant difference between the pre-test and post-test scores of the students in Control group. As evident from the out-come of the research, the Process Approach (which presents writing in multiple drafts before the final writing) had significant effect on students’ overall performance in essay writing.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".