Classroom Process and Causes of Worsening High School Students' Failure of Literature-in-English in Nigeria
Bibliographic record
Abstract
Current theories of teaching favour students-centred classrooms. In Literature-in-English lessons, active participation of students is essential for optimal learning. This study examined the level of students' active participation in Literature-in-English classrooms as the major reason for the high failure rates of Nigerian students who take this subject in the yearly senior secondary school examinations. The study was carried out in Ibadan metropolis, Oyo State. The mixed method research design was adopted. Class Observation Schedule (r=0.9) and Interview Guide for Teachers were used to gather the data. Random sampling technique was used to select three Local Government Areas (LGAs), 27 public senior secondary schools (9 from each LGA) and one Literature-in-English teacher from each school. All the teachers were observed twice during lessons while 12 of them were interviewed. Results from the quantitative data showed that the classroom process was teacher-dominated, and students' level of participation was very low. Students hardly asked questions and teachers did not use teaching techniques that encouraged active participation. Teachers identified students' inability to buy the prescribed literary texts, lack of interest in reading and poor language proficiency as some of the factors that contribute to students' failure. Several recommendations were made to enhance the teaching-learning process and maximize learning.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".