General Preference and Senior Secondary Schools Literature-in-English Achievement
Bibliographic record
Abstract
This study investigated the extent to which general preference of students would predict their achievement in literature in English in selected senior secondary schools in Ibadan. Five research questions were asked while descriptive survey design was adopted for the study. Simple random sample technique was used to select 500 students offering Literature in English in ten senior secondary schools in Ibadan Metropolis. Two research instruments were used in the study namely general preference questionnaire r = .70 and Literature in English Achievement test r = .81. The data collected were analyzed using frequency count, simple percentages, and regression analysis. The result shows that: the student preferred prose to any other genre (X = 2.57); the preference for prose has significant contribution to the achievement of the students in Literature in English. (b = 0.463; + = 8.472; p < 0.05) and prose literature is the only genre capable of predicting students’ achievement in Literature. Based on the findings, recommendations were made that students’ interests should be developed in other genres not preferred through good instructional strategies for optimal performance. Key words : General preference; Predictor; Achievement; Prose; Poetry; Drama
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.001 |
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".