A Mixed-Method Study on the Impact of ELSA Speak in Enhancing Oral Communication Skills of Introverted ESL Students
Bibliographic record
Abstract
This research investigates how the ELSA Speak application enhances spoken English competencies among introverted ESL learners, with particular emphasis on first-year university students and their public speaking capabilities. Employing a mixed-methods research design, the study examined 93 ESL participants aged 20-22 years to evaluate their receptiveness toward utilizing ELSA Speak for public speaking skill development. Participants underwent random selection and engaged in structured ELSA Speak practice sessions, subsequently being allocated into control groups (comprising extroverted learners) and experimental groups (comprising introverted learners). Data collection utilized pre-assessment and post-assessment evaluations, with statistical analysis conducted through paired-sample t-tests and one-sample t-tests. Results demonstrated statistically significant enhancement in speaking proficiency during post-assessments, particularly evident in digital communication contexts and public speaking scenarios. Post-assessment analysis revealed that ELSA Speak's comprehensive feedback mechanisms enabled participants to recognize and remediate specific performance deficiencies. The research advocates for integrating ELSA Speak functionality with virtual conferencing platforms to strengthen both general and professional communication competencies. Findings underscore ELSA Speak's efficacy as an instrumental resource for advancing language proficiency among introverted ESL students, especially within professional contexts, while encouraging additional investigation into its applicability across various academic fields beyond oral communication skills.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".