A Qualitative Study on the Best Motivational Teaching Strategies in the Context of Oman: Perspectives of EFL Teachers
Bibliographic record
Abstract
In the pedagogical process, lack of motivation becomes a controversial issue. In this qualitative study, the researcher intended to explore the best motivational teaching strategies from the perspectives of the EFL teachers at Buraimi University College (henceforth BUC) in Oman. Purposeful sampling was used in the selection of five EFL teachers in English language department at BUC. The protocol of interview (semi-structured interviews) was conducted for data collection. The obtained data provided by the participants was analysed using qualitative thematic analysis to answer the questions and accomplish the objective of the present study. The findings revealed that motivational strategies would not be applicable in any classroom without creating a helpful, interactive, engaging, and enjoyable environment. The participants of the current study believe in the vital role of playing games in stimulating the interest and enthusiasm of learners as well as giving fun and energetic environment. Besides, it is essential to inspire students with the feelings of a sense of accomplishment through helping them to set realistic goals according to their learning abilities and observe their progress once in a while. However, in any learning environment developing motivation is a difficult task for the teachers because students learn differently and every student is diverse in her way.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".