Teach Smarter, not Harder: A Call for Empowering EFL Teachers with Strategies to Activate Learner-Centeredness
Bibliographic record
Abstract
Learner empowerment entails making them autonomous in both learning and living and this is also the fulcrum on which the post-pandemic educational paradigm rests. Teachers in the contemporary times are required to train their students on the strategies of lifelong learning and become self-learners. Therefore, this study aims to gauge the perceptions of 110 EFL teachers at Qassim University on their level of motivation and empowerment for their B.A students. The study also establishes correlation between motivation and student empowerment. A quantitative research design is applied here to achieve the goals of the study. A reliable and validated 20-close ended questionnaire items is administrated to the participants using Google Forms and precise data sought. The study results show that EFL teachers at Qassim University have a high positive perception in motivating their students to English learning with a total average of (M=4.11). Furthermore, the study also reported a high level of student empowerment with strategies of self-learning reaching (M=3.90, STD=.708). Finally, a strong and direct correlation was found between motivation and empowerment of Saudi EFL students, in which Pearson coefficient was computed at (.841) and the probability value (Sig.=.000). Accordingly, it is recommended that EFL teachers use active strategies which motive and empower their students to be centered in the learning process.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".