A Systematic Review of Research on Questioning as a High-level Cognitive Strategy
Bibliographic record
Abstract
Given the significance of questioning as a high-level cognitive strategy in language teaching and learning in the literature on TEFL as well as in education in general, this study sought to make a systematic review of research studies conducted in the span of the last three decades on the issue of questioning across different disciplines with a special focus on second or foreign language teaching and learning. It encompasses the questioning behavior of both teachers and learners. In the first phase of the study, it reviews and synthesizes the findings of 60 studies conducted on questioning in education since 1974. It also illustrates the impact of different questioning patterns on various types of learning and literacy areas. In the second phase of the study, an in-depth review is made of 40 studies between 2000 and 2014 examining the role of questioning in different academic fields and various educational fields. The findings of the in-depth review reveal the indispensable role of teacher and student questioning in facilitating critical thinking, writing ability, reading comprehension, subject matter learning, metacognitive skills, and scaffolding learning process. Finally, the implications and applications of the research findings are mentioned along with suggestions for further research.
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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".