Voice Typing to Enhance English Intermediate Level Learners’ Speaking Proficiency: A Case Study
Bibliographic record
Abstract
This mixed-methods case study explores the effectiveness of Google Docs’ voice typing feature in enhancing the English oral skills of nine university students at the intermediate level. These university students were selected to utilize voice typing for their end-of-term oral presentations, engaging in two practice sessions each week for six weeks period. Training was provided to ensure proficiency in the use of voice typing. To gauge progress, a qualitative analysis of the data was performed, focusing on any advancements in speaking capabilities, with the students’ final speaking project scores serving as the primary metric for evaluation. Evaluation of the final speaking project was conducted by two teachers, and to validate the assessment process, inter-rater reliability tests were implemented. In addition to the quantitative assessment, qualitative data were gathered through interviews, wherein students conveyed their initial challenges with the voice typing feature, particularly concerning speech recognition inaccuracies. Despite these initial hurdles, participants reported a smoother experience as they became more accustomed to the tool. The study underscores the educational benefits of voice typing and advocates for continued investigation into this promising field, particularly given the rise of Artificial Intelligence technologies adept at accommodating diverse accents and proficiency levels.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".