To praise or not to praise? Examining the effects of ability vs. effort praise on speaking anxiety and willingness to communicate in EFL classrooms
Bibliographic record
Abstract
This study examined the effects of praise for intelligence and praise for effort on Iranian EFL learners’ language mindsets, perceived communication competence, speaking anxiety, and willingness to communicate (WTC). The students in three English classes (N = 63, all junior high school students) in a private language institute filled in self-report scales on language mindsets, perceived communication competence, speaking anxiety, and WTC, and then were assigned to praise for effort, praise for intelligence, and control conditions for 14 classroom communication sessions. They answered the same scales in the last session of the experiment. The results of quantitative analysis indicated that praise for effort enhanced learners’ growth mindsets, communicative competence, and WTC, and decreased their speaking anxiety. In contrast, praise for intelligence and no praise decreased students’ growth mindsets. Praise for intelligence further decreased students’ WTC and increased their speaking anxiety. Follow-up qualitative data gathered by interviews with 12 students further suggested that praise for effort facilitated learners’ WTC by increasing their growth mindsets and lowering their speaking anxiety. Finally, we discussed practical implications for how language teachers enhance students’ success in classroom communication.
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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 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".