The Impact of Using Short Films on Learning Idioms in EFL Classes
Bibliographic record
Abstract
Having a good command of idioms of a foreign language is regarded as an important element in mastering that language. However, due to their opaque nature, extracting idioms’ figurative meaning appears to be a challenging endeavor for most foreign language learners. Grounded in the cognitive theory of multimedia learning, this study aimed to investigate the extent to which using short films has an impact on Saudi EFL learners’ receptive knowledge of idioms. Data were collected from 84 female undergraduate students at a university in Saudi Arabia using a pre- and post-test, a questionnaire, and a semi-structured interview. The findings revealed that the participants in the short films group significantly outperformed their counterparts in the blogger group. Moreover, the findings also showed that the participants had positive attitudes toward using short films to study idioms. In attempting to tackle the issue of how English idioms can be learned and taught effectively in the language classroom, the research findings provide helpful insights for both EFL teachers, learners, and curriculum designers. It is recommended for English teachers to become more acquainted with and adopt more flexible and engaging pedagogical methods such as short films in teaching idiomatic expressions to their learners. Employing short films as a complementary teaching material can further ease the burden of idiom learning and create a more motivating and engaging learning environment for the learner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".