Teachers’ Qualification and Questioning Styles as Correlates of Achievement in Reading in Secondary Schools in Ido Lga, Nigeria
Bibliographic record
Abstract
The study examined teachers’ qualification and questioning style as determinants of senior secondary students’ achievement in reading comprehension in Ido LGA, Nigeria. Studies have shown that several teacher-related factors, especially subject mastery, experience, qualification, and questioning styles contribute to students’ learning outcomes in ESL classrooms. Hence, there is the need to conduct this study. The study adopted a correlational type of descriptive research design. Four research h questions were raised and 20 English language teachers were randomly selected to participate in this study. Two research instruments were used: Teachers’ Questionnaire on Pedagogical Factors in Teaching (TQPFIT) (r = 0.74) and Reading Comprehension Achievement Test (RCAT) (r = 0.71) were used for data collection. The results indicated that there was no significant relationship between the in dependent variables and students’ achievement in reading comprehension. Also, there were no relative and composite contribution of teachers’ qualification and questioning styles to students’ achievement in reading comprehension. However, questioning styles predicted students’ achievement higher than teachers’ qualification. Based on these findings, it was recommended that teachers should develop and adopt quality questioning styles during lesson delivery to stimulate learners’ interest, engage them in the learning task, and improve students’ achievement.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".