Attitude toward Enhancing Extensive Listening through Podcasts Supplementary Pack
Bibliographic record
Abstract
To promote independent extensive listening, the aim of this study is to investigate Saudi preparatory level students’ and their teachers’ perception about podcasts’ criteria and contents to include in an extensive supplementary listening pack. An exploratory sequential design was adopted to collect data. The results of the focus group thematic analysis helped designing an online close-ended survey. The participants were 120 students and teachers sampled from the four proficiency levels of the English Language Institute (ELI) at King Abdulaziz University (KAU) in the Kingdom of Saudi Arabia (KSA). The findings of the study revealed that teachers were more familiar with the podcasts than students. Furthermore, all participants had a positive attitude toward using a listening instructional supplementary pack that can include few short authentic podcasts. They recommended using various challenging topics that are related to students’ interests and proficiency levels. This study contributes to the literature of integrating podcasts to enhance extensive listening. It recommends designing an extensive listening supplementary pack based on Vandergrift and Goh’s (2012) metacognitive approach and testing its suitability for application.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".