The Effect of Length of Exposure to CALL Technology on Young Iranian EFL Learners’ Grammar Gain
Bibliographic record
Abstract
In the twenty-first century, integration of technology into education is a force worthy of contemplation. Among all the possible technological tools that can be integrated into EFL classes, computers seem to have achieved a more dominant position. One of the outstanding features of computers is their potential to present educational games and to add fun to grammar learning. This study investigated the possible effects of the integration of CALL technology on young Iranian elementary EFL learners' grammar gain. Moreover, it examined the role of length of exposure to find an optimum balance for the proper amount of CALL integration to language activities. One control and two experimental groups, each consisted of 15 participants, were engaged. One of the experimental groups used CALL technology for twice as long as the other group. 'Family and Friends 2' with its accompanying MultiRom was utilized in the experimental groups. This software presented computer-based grammar activities. All the participants tried their answers in separate immediate as well as delayed post-tests. The results of one way ANOVA demonstrated significant differences between control and experimental groups in the immediate post-test. The findings of the delayed post-test showed that a significant difference did exist between the control group and the second experimental group. Furthermore, the length of exposure was found to be influential. The results of this study provide some insights for teachers and administrators to review their curricula, approaches, and educational tools, and to consider the possibility of incorporating CALL technology into their teaching environments.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".