Effects of EFL Teachers’ Self-efficacy on Motivational Teaching Behaviors
Bibliographic record
Abstract
This study examined the predictability of EFL teachers’ self-efficacy on their motivational teaching behaviors. Participants involved 112 English teachers from China’s tertiary education institutes selected by a “snowball” sampling strategy and also from an EFL teacher training camp. They completed a questionnaire survey of the demographic information of participants, their perception of self-efficacy, and their motivational teaching behaviors in English classroom. Results from the descriptive statistics and a multiple regression analysis were generated based on the survey. It indicated that college EFL teachers perceived themselves with much higher self-efficacy for instructional strategies than efficacy for classroom management and efficacy for student engagement. Also, the results revealed that two most frequently used motivational strategies by teachers in language classroom were strategies for generating students’ initial motivation and strategies for maintaining and protecting students’ motivation. The results also showed that teachers’ self-efficacy significantly contributed to the prediction of teachers’ motivational teaching behaviors and accounted for more than one third of the variance to teachers’ motivational teaching behaviors. This study confirmed that there was a direct causal relationship between English teachers’ perceptions of their self-efficacy and their adoption of motivational strategies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".