Enhancement of Learners’ Receptive Skills through Task-Based Activities to Understand the Eco-Centric Issues Using Wall-E
Bibliographic record
Abstract
Animation films inspired by eco-centric ideas, such as green studies, pave the way for learners to explore the problems caused by humans to nature, such as air pollution, deforestation, climate change, and trash pollution. On the other hand, animated movies also help to motivate and develop learners reading and listening skills. This study focuses on developing learners’ receptive skills using the eco-centric theme-based animation movie “Wall-E” through task-based activities (TBA). TBA is a technique that helps students prepare themselves by having participants evaluate their goals for listening, understanding information, and developing their language needs. This study aims to examine the effectiveness of understanding the eco-theme in the animation movie Wall-E and to enhance learners receptive skills using TBA. A pre-experimental study was carried out with a one-group pre-test and post-test. The study samples were selected using purposive sampling. Initially, a pre-questionnaire was circulated to 103 participants with Arts backgrounds to test their language proficiency and understanding of eco-centric ideas. Based on the results obtained from the pre-questionnaire, 36 participants were subjected to a pre-test, after which activities were given using TBA during tasks or task cycles, language focus, and finally a post-test. Each activity focuses on reading and listening tasks to develop the learners’ comprehension, thereby promoting and understanding ecological issues from the movie. The result of the study shows that there is a gradual improvement in the mean score between the paired-t test of pre-test and post-test, which is (t [36] = 6.13889, P< 0.001), the P value being less than 0.005. The present study was suggestive that TBA effectively developed learners’ receptive skills and also motivated learners about environmental issues in the movie Wall-E.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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".