Using Movies in EFL Classrooms: A Study Conducted at the English Language Institute (ELI), King Abdul-Aziz University
Bibliographic record
Abstract
<p>The present study sought to examine the attitudes of Saudi English as a foreign language (EFL) learners as well as teachers towards the integration of English movies in their classes as a tool to develop students’ language skills. Fifty female intermediate level students studying English in their Preparatory Year Program (PYP) in the English Language Institute (ELI) at King Abdul-Aziz University (KAU), Jeddah, Saudi Arabia, participated in the study. Questionnaires were administered to the students to investigate their perceptions towards the integration of English movies in their classes to develop their language skills. The researcher also conducted semi-structured interviews with both students and teachers to explore their perceptions towards the use of movies in their classes. In addition, teachers were required to write reflective journals regarding the use of movies in their classes. The findings of the study indicate that both students as well as teachers had positive attitudes towards the use of movies in their classes to improve students’ language skills. The study offers pedagogical implications for EFL instructors with respect to the integration of films in their classrooms to improve students’ language learning. Well-selected movie materials could enhance students’ language learning process and increase their motivation to learn the target language.</p>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".