Establishing the Methods of Poetry Teaching to Influence Critical Thinking among Secondary Schools' Learners in Kiambu County, Kenya
Bibliographic record
Abstract
Methods of poetry teaching has not only the potential to influence Critical Thinking (CT), but it also enables students' self-assurance and independent problem-solving skills. Nevertheless, teachers and students the around the world, share the opinion that poetry is difficult to understand and that only literary experts can appreciate it. The objective of the study was to establish the methods of poetry teaching among secondary school learners in Githunguri Sub-County, Kiambu County. The study was based on Vygotsky’s social constructivist theory. The research adopted a descriptive survey design. The research was conducted in 10 public secondary schools with the sample size of 10 English teachers and 349 Form three students. Schools were chosen using stratified random sampling and teachers were selected purposively. Simple random sampling was used to select the students’ sample. Data was collected from teachers and students using questionnaires, structured interviews and observation check-list. Data was analysed using descriptive statistics, which included tables, pie charts, frequency, mean, and standard deviation. For qualitative data, thematic interpretation was done and documented either in tables or pie charts while quantitative data was analysed using statistical analysis or tabulation. The study established that although teachers mostly teach poetry using interactive classroom setting, disparities were observed on students’ responses. Students reported that they did not work in groups to solve problems in the process of poetry teaching. In addition, the study found out that there was limited use of classroom discussion, brainstorming, group work, analysis of poetic devices, asking questions, evaluating, analysing, and interpreting poetic texts. The study therefore recommends that teachers should continue teaching poetry using different methods as this is likely to enhance student’s CT
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".