A Study of Lower-order and Higher-order Questions at Secondary Level
Bibliographic record
Abstract
In classrooms, questioning is one of the most regularly employed teaching strategies. Questioning is considered by many to be the most important tool that teachers have for helping students build understanding and to encourage students to think about and act upon the material that have structured. Questions are asked to individual pupils, to the whole class, to small groups to arouse curiosity, focus attention, develop an active approach, stimulate pupils, structure the task, diagnose difficulties, communicate expectation, help children reflect, develop thinking skills, help group reflection, provoke discussion and show interest in pupils’ ideas. Perhaps few studies have been carried out regarding the levels of classroom questions in Pakistan on this vital aspect of teaching learning process. The main objective of the study was to explore the levels of questions teachers asked during teaching at secondary level using bloom’s taxonomy. It was focused to observe the ratio of lower and higher- order questions. It was an observational study of the descriptive method. The target population comprised all the teachers of Working Folks Grammar School & College Peshawar. Twenty teachers of different subjects teaching at secondary level were randomly selected as sample of the study. Teachers were observed using an observational guide and audio recording were conducted. The researcher focused only the asked questions of the teachers. This study is significant because its findings and conclusions may stimulate teachers to improve their questioning behavior in order to develop and promote higher order thinking abilities. The result of the study showed that so much time was spent with teachers questioning the students. Most of the questions were low- level cognitive questions. Higher- order questions were also observed however, the ratio of these questions was very low. Total percentage of questions during 445 minutes was 60 percent. The whole number of questions was good but in most of the classes the number of questions was low. Among 267 questions 67 percent were knowledge based, 23 percent were comprehension based, 7 percent were application based, 2 percent were analysis based and 1 percent was synthesis based. However the ratio of evaluation based questions was zero.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".