Enhancing Poetic Literature Instruction Through Stylistic and Thematic Approaches
Bibliographic record
Abstract
This study investigated the effects of instruction in stylistic and thematic approaches on students’ achievement in poetic literature. It also determined the moderating effect of gender on students’ achievement in poetry. The study adopted pretest, post test, control group quasi-experimental design. The participants in the study were SS11 students in Akure metropolis. The subjects were randomly assigned to two experimental groups and one control group. The instruments used were Students’ Achievement Test in Poetry (r=0.75), Instructional Guide on Stylistic Approach, Instructional Guide on Thematic Approach, Instructional Guide on the Convention Lecture Method, and Teaching Observational Scale. Three hypotheses were tested at the 0.5 alpha levels. Data were analyzed using means, standard deviation, Analysis of Covariance (ANCOVA) and Scheffe post hoc test. Findings revealed that there was significant main effect of treatment on students’ achievement in poetry (F (2,125) =25.915; P .05. The interaction effect of treatment and gender on students’ achievement in poetry was, however, not significant (F(2,125=.577; P>.05). Based on these findings, it is recommended that teachers should adopt stylistic and thematic approaches in the teaching of poetry in particular and literature in general.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".