Diploma Students’ Perceptions Regarding the Effectiveness of Using an English-Speaking Practice Application on Their Primary Skills
Bibliographic record
Abstract
This paper endeavored to examine the validation of the English-Speaking Practice Application in developing the speaking skills of English as a Foreign Language (EFL) learners. According to the author, the Desuggestopedia teaching method—which strives to establish a comfortable and relaxing learning environment that improves language acquisition—is the basis for this program. Forty-four diploma candidates from Saudi Arabia's Qassim University participated in the study. Over 12 weeks, they spent 30 minutes a week using the app. Using a survey questionnaire, they were asked to rate the app's usefulness, efficacy, and motivational effects. The internal consistency of the questionnaire was high (Cronbach's alpha = 0.89). According to the study's findings, EFL diploma students had a favorable opinion of the English-Speaking Practice App in terms of its usefulness, efficacy, and motivational influence. This study advances the field of language instruction and emphasizes the possibilities of technology-enhanced language learning. The findings of this study can help practitioners and educators create and apply useful tools and techniques to enhance the speaking abilities of EFL students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".