THE USE OF FILM TRAILER IN TEACHING WRITING NARRATIVE TEXT
Bibliographic record
Abstract
This research was aimed at investigating the use of film trailers in teaching writing narrative text. A quasi experimental design was employed with two classes of the ten graders at one school in Bandung selected purposively as sample. The data were obtained through a pre-test, a post-test and an interview. The writing assessment was adopted from Alberta Education (2009) covering organization, and vocabularies. The findings were analyzed statistically using independent t-test procedure and Gebhardt theories on film trailers (2004). The statistical computation showed that film trailer was likely effective in improving the students’ writing skill in narrative text (tobt 5.001 > tcrit 2.000, r = 0.63). Furthermore, there were six positive perception of the use of film trailer in writing revealed: writing skills improvement covering context (text structures), organization (idea), and vocabularies; creating challenging atmosphere; inspiring students on writing and increasing students’ motivation. In addition, the obstacles found were the duration of film trailer, and native speaker language comprehension. Designing the learning purpose appropriately which considers students’ needs and language level may ease teachers to use the film trailers in improving students’ writing skills. The teachers may try to take advantage of film trailer to be utilized in their classroom activities to conduct an effective and interesting learning atmosphere in the classroom. Key words: film trailer, narrative text, teaching writing, senior high school
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".