Supporting EFL Students’ Learning of Theoretical English-Content Through Using an Inquiry-Based Teaching Technique
Bibliographic record
Abstract
The present study investigates the effects of implementing an inquiry-based teaching technique on motivating EFL/ESL student teachers to learn an English content-based course and to become critical toward the knowledge they are exposed to in this course. A quasi-experimental methodology of research was implemented through the one independent group design on a class of 19 students majoring in English education in the bachelor program at Bahrain Teachers College, University of Bahrain. A pre-post questionnaire was conducted to identify students’ motivations towards both the English content-based course and the proposed technique that is the inquiry-based teaching before and after the application of it. The study findings revealed positive impact of the proposed technique on increasing those EFL/ESL student teachers’ motivations toward the current course which indicates the effectiveness of this technique in motivating students to study other theoretical English content-based courses as well as supporting these students to develop new study skills that can assist them to learn and understand the content of these types of courses, which usually are theoretical.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".