The Impact of Computer Assisted Language Learning on Iranian EFL Learners’ Task-Based Listening Skill and Motivation
Bibliographic record
Abstract
The application of technology in language classrooms has become more commonplace in the last fifty years. Computer and the internet have made foreign language materials easy to access and use. We build on this growing body of research by presenting the findings of a research project that explored the effect of computer assisted language learning (CALL) on improving Iranian EFL learners‟ task-based listening as a motivating device to enhance formation of positive attitudes. The participants in this quantitative study included 40 EFL learners of English as a foreign language (EFL) at Islamic Azad University – Tabriz Branch. They were taking the two-credit conversation course 2 and formed two intact classes which were randomly assigned as the experimental and the control groups. During the CALL-based treatment each participant in the experimental group had an access to a computer in the English lab. They also received extra task-based listening comprehension materials and activities along with some comprehension questions three times a week through their e-mails. The data analysis of the post-test listening comprehension scores indicated a significant difference between the experimental and control groups; that is to say, the experimental group outperformed the control group and obtained a higher average. The motivation of the experimental group participants was also higher than the control group participants.
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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 | 0.004 |
| 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".