Classroom Participation and Study Habit as Predictors of Achievement in Literature-in-English
Bibliographic record
Abstract
Monovariate studies have confirmed the positive influence of classroom Participation and Study habit on students’ academic achievement in general. However, the extent to which each of these variables could predict students’ achievement in Literature in English has not being a focus of much research attention. Hence, this study attempted to investigate the extent to which classroom participation and study habits predicted students’ academic achievement in Literature-in-English in selected senior secondary school in Ibadan North Local Government Area of Oyo State.Five research questions were raised to guide this study. The study adopted descriptive research design of ex-post facto type. The sample comprised 500 senior secondary school two (SSS2) students from ten selected secondary schools in Ibadan North Local Government Area of Oyo State. The three instruments used for data collection were students classroom participation scale (SCPS) (r = 0.79), students study habits questionnaire (SSHQ) (r = 0.76) and Literature-in-English achievement Test (LAT) (r = 0.74). Data collected were analyzed using Pearson Product Moment Correlation (PPMC) and Multiple Regression Analysis. The results were interpreted at P<.05The results revealed that: there was a significant relationship between classroom participation and students’ achievement in Literature in English (r =.134, df = 498; p<.05); there was no significant relationship between study habits and students’ achievement in Literature in English (r=.042, df= 498; P<.05); there was a significant relative contribution of classroom participation on students’ achievement in Literature in English (β =.131); there was no significant relative contribution of study habits on students’ achievement in Literature in English (β = 0.21); there was a significant composite effect of classroom participation and study habits on students’ achievement in Literature in English (β =.134;F 1,498 = 4.663; P <.05). The findings finally indicated that classroom participation was the only variable that predicted students’ achievement in Literature in English. Based on the findings, it was commended that teachers should allow students to contribute and share ideas freely among their colleagues while Curriculum planners should suggest teaching and learning activities that could give room for students’ active participation in class when designing Literature in English curriculum.
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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 | 0.001 | 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) | 0.003 | 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".