Improving EFL Learners’ Pronunciation of English through Quiz-Demonstration-Practice-Revision (QDPR)
Bibliographic record
Abstract
This study investigates the effectiveness of Quiz-Demonstration-Practice-Revision (QDPR) in improving EFL learners’ pronunciation of English. To achieve the goal, the present researcher conducted a one-group pretest-posttest design. The experimental group was selected using a random sampling technique with consideration of the inclusion criteria. Through the treatment process, the group was taught using QDPR in which the student participants were explained how to pronounce the target English phonemes in their first language (L1). The student participants were given an oral test and a written test related to the target English phonemes and a questionnaire on QDPR. The collected data were analyzed using paired-sample t-tests to examine the significant difference in the means scores of their knowledge of pronunciation and their ability to pronounce the target English phonemes, and simple regression tests to investigate the effectiveness of QDPR learning model to their knowledge of pronunciation and their ability to produce the target phonemes. The results of data analysis have revealed that (1) QDPR was significantly effective in improving EFL learners’ pronunciation, and (2) QDPR significantly helped the students improve their pronunciation. Thus, QDPR can be an alternative model to English pronunciation instruction in EFL classrooms.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".