The Effect of CALL on Iranian Beginner EFL Learners’ Grammar Learning
Bibliographic record
Abstract
Using computer programs has recently caused language teaching and learning to undergo influential changes. Computer proved to be an instrument for those who are willing to learn a foreign language. It should be pointed out that CALL programs certainly have helped to educators to develop different types of learning which is based upon these technologies. Computer-Assisted Language Learning (CALL) is among those programs, which has caused this great change. The present study had a quasiexperimental design and involved quantitative data collection procedures. Participants were males and were selected from second grade students of a guidance school in Guilan. The purpose of the present research was to investigate the effect of CALL on improving beginner EFLs' grammar learning. To this aim, a multiple-choice test of grammar namely KET, of which the reliability was 0.79, was administered to 127 EFL beginners out of whom sixty-four of the students were selected as homogeneous and randomly divided into two groups of thirty-two participants. Assigned groups were the control group that were taught with traditional grammar learning methods and an experimental group who underwent the CALL instruction. The results of the study through a posttest revealed that the experimental group outperformed the control group. Therefore, CALL appeared to be useful in developing English grammar of the EFL students. The researcher concluded that integrating technology to curriculum is beneficial for beginners and that the application of computer and its related technologies can facilitate grammar learning both inside and outside the classroom.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".