The Effect of CALL on the Vocabulary Learning of Iranian EFL Learners
Bibliographic record
Abstract
The present study was intended to investigate the effectiveness of CALL on Iranian EFL learners' vocabulary learning in two institutes in Tehran, Iran, as compared to those students receiving traditional instruction using the printed text materials. CALL (Computer Assisted Language Learning) has given man versatility in many areas, and seems the paramount representation of technology for today. The goal of the study was to examine the effects of the application of CALL on students،attitudes towards CALL before and after the instruction. To carry out the study, 60 homogeneous male and female participants were selected from among students and randomly assigned into two groups, the traditional group and CALL group. A vocabulary achievement test as pre-test was administered to participants of both groups. The results of t-test confirmed that there is no significant difference between the participants regarding their vocabulary knowledge. The Computer Assisted Instruction group experienced 16 sessions of instruction using the CALL. The traditional instruction group received the same hours of instruction and materials but on paper with no audio-visual features. The result of paired sample t-test between pre-test and post-test of both groups of study revealed that there is a significant difference between experimental and control group regarding their vocabulary knowledge. CALL instruction improved EFL learners' knowledge of vocabulary. Besides, the results of descriptive statistics showed that the group who received Computer Assisted Language Learning was outperformed in this study.
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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 | 0.000 | 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".