The Effect of Brain-Based Teaching on Young EFL Learners’ Self-Efficacy
Bibliographic record
Abstract
The present study aimed to investigate the effect of brain-based teaching on the self-efficacy of young EFL learners. The initial participants of the study were 90 learners within the age range of 13-16 who were selected based on convenience sampling. Theses 90 young EFL learners were given a Flyers test the scores of which were used to choose 60 homogeneous learners whose scores fell within the range of +/- one standard deviation from the mean. The 60 selected learners were then divided into an experimental and a control group. A Self-Efficacy Questionnaire for Children (SEQ-C), developed by Muris (2001) was administered to the participants in both groups before and after the treatment after being translated into Persian and piloted for reliability check. To implement brain-based teaching in the experimental group, the researcher taught the lessons based on the three techniques of Brain Based Teaching Approach (BBTA). The three techniques used were Relaxed Alertness (RA), Orchestrated Immersion (OI) and Active Processing (AP) in line with Thomas and Swamy (2014). The results of statistical analyses indicated that brain-based teaching approach had a significant effect on students’ self-efficacy. The findings of the study can have implications for both students and EFL teachers in the realm of foreign language learning and teaching.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".