The Effect of Post-Teaching Activity Type on Vocabulary Learning of Elementary EFL Learners
Bibliographic record
Abstract
Considering the significant role of vocabulary in learning a language, and teachers' great responsibility in providing opportunities to facilitate this learning, many studies have focused on the best means of achieving a good knowledge of vocabulary. This study set out to investigate the effect of four post-teaching activities, namely game, narrative writing, role-play, and speaking tasks on vocabulary gain of elementary Iranian EFL learners across gender. The sample in the study was composed of 111 elementary adult EFL learners assigned into four experimental groups for females and four experimental groups for males as well as two control groups one for each gender, at AVA Talk Institute, Urmia, Iran. Successive to the pre-test, which was meant to measure the learners' prior knowledge of the target words, learners were asked to carry out the required tasks using the words they were provided with. The results of two-way ANOVA analysis indicate statistically significant main effects for vocabulary learning across different activity types with role-play leading to the highest vocabulary gain (M=19.27, SD=3.70). Moreover, the gender of participants has a significant [F (1, 168) =28.40, p=.000] impact upon the vocabulary learning of the participants, with female learners outperforming their male peers. The results of the study have implications for EFL teachers and provide them with new insights into implementing task-oriented activities for better retention of vocabulary.
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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 | 0.000 | 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 | 0.001 | 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".