The Impact of Podcasts on EFL Students’ Listening Comprehension
Bibliographic record
Abstract
This research has objective to investigate students’ listening comprehension through the use of podcast in EFL classroom. 60 high school students in Indonesia were taken as sample for this research with distribution of 30 students in experiment class and 30 students in control class. The samples were taken by using cluster random sampling. Quasi-experimental method with the post-test only control group design was applied in this research. In addition, a survey questionnaire was administered to experimental group to explore their perception on the use of podcast instruction in teaching listening. Findings revealed that there is a significant difference of post-test score between two groups, favoring experimental group. Data analysis using one way ANOVA showed significance value (sig. 0.010) is lower than < 0.05 which interpreted that Podcast has significant impact on students’ listening comprehension. Additionally, the result of questionnaire indicated that students have positive attitude toward the use of podcast in listening classroom. Students perceived that podcasts provided authentic materials, interesting activities including listening exercises and meaningful tasks for them so they felt more motivated to learn English. This study recommended that teacher may utilize podcast in teaching listening considering its effectiveness as technology based learning tool.
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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 | 0.006 |
| 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) | 0.005 | 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".