The Effect of Corpus-Based Language Teaching on Iranian EFL Learners’ Vocabulary Learning and Retention
Bibliographic record
Abstract
The use of corpora in second/foreign language (ESL/EFL) classes has established to be a valuable tool in teaching grammar, vocabulary and natural language use. The corpus-based approach to language teaching and linguistics has gained its prominence since the mid-1980s. However, there has been little research on investigating the corpus-based tasks openly in the classroom. The current research attempts to examine the effect of corpus-based teaching on EFL learners’ vocabulary learning and retention of Iranian EFL learners. Forty pre university Iranian female students at Saei high school in Gorgan, aged 18 participated in this study. The number of participants in each group was 20. After administering the pretest, students in the experimental group were taught using corpus-based approach while students in the control group were taught using traditional methods. After instruction, a posttest was administrated to both groups. After two weeks of the first posttest, the second posttest was administrated to both groups to see the effect of corpus-based teaching on vocabulary retention (immediate retention). The design of the study was quasi-experimental, as there was no random selection. T-tests were employed to analyze the collected data from the vocabulary tests including pretest and posttests. The results of the study indicated a significant difference between the experimental and control group in favor of corpus-based vocabulary teaching. The result also showed that corpus-based teaching has a significant effect on EFL students’ vocabulary retention and the effect did not fade away over time. This study has some pedagogical implications which can bring fruitful results for language teachers and learners and material developers.
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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 | 0.006 |
| 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) | 0.001 | 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".