The Impact of Shadowing Technique on Tertiary EFL Learners’ Listening Skill Achievements
Bibliographic record
Abstract
This paper is aimed at describing the impact of Shadowing Technique on students’ listening skills achievement. Therefore, the experimental research was conducted and the techniques on collecting the data were administrating pre- and post- tests to the experiment and control groups, which consisted of 30 university students in each group. Then, t-test and ANCOVA were applied on analyzing the data, then to find the impact of shadowing technique on EFL learners’ listening skill achievements in English Department of Universitas Negeri Medan (UNIMED), Indonesia. As a result, there was a significant difference between the mean of experimental and control groups (F = 8.98, p=. 004 < .05). In addition, there was a significant effect of applying shadowing technique on students’ listening skill achievements (F=56.10, p=0.00<0.05) and the experimental group grammatically outperformed the control group. In conclusion, the null hypothesis was rejected and the alternative hypothesis was accepted.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".