A Phenomenological Study: The Impacts of Developing Phonetic Awareness through Technological Resources on English Language Learners’ (ELL) Communicative Competences
Bibliographic record
Abstract
Throughout our experience within the English Language Teaching (ELT) field and while acquiring a second language in English a Foreign Language (EFL) and English as a Second Language (ESL) settings, we have noticed that one of the main perceived challenges for English Language Learners (ELLs) is to effectively communicate. Most of the time, this issue comes from the concern or fear to mispronounce any word, considering that English manages some variations on its phonetic alphabet, which differs from other languages. Therefore, it becomes necessary for ELLs the acquisition of English phonemic awareness to improve their pronunciation, fluency, and confidence when orally communicating in English. Basing on the interlanguage hypothesis, phonemic awareness, English phonology theories, and Information and Communication Technology (ICT) tools and resources, this study aims to analyze: a.) to what extend does phonemic awareness development influence on students’ communication skills? b.) How effective is the implementation of technology to develop phonemic awareness? To do so, a phenomenological study, based on the constructivism epistemology, was conducted including a deep revision of the existed literature, various studies previously applied, and the researchers’ experience within the teaching and professional field to examine the impacts of developing phonetic awareness through technological resources on English language learners’ (ELL) communicative competences.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".