Bibliographic record
Abstract
Writing involves more than attention to form (e.g., orthography, grammar), since it requires attention to text type, content, and genre. However, most students of English as a second language (L2) tend to prioritize linguistic accuracy in their writing, to the detriment of the content of their texts. Automatic speech recognition (ASR) has the potential to mitigate this, as it reduces the cognitive burden of writing by facilitating the text input process (using a skill most humans possess—speaking), offering assistance in spelling, and allowing a focus on other aspects of the task (e.g., cohesion, content). Automatic speech recognition is not only accessible and free, but it also fulfills Chapelle’s (2001) criteria of an effective computer-assisted language learning tool (e.g., authenticity, learner fit). Despite these affordances, there is a dearth of studies examining the possible affordances of ASR for writing. This mixed methods, one-shot study examines L2 writers’ perceptions of using ASR to write using the technology acceptance model (TAM). Seventeen (N = 17) undergraduate students at a Canadian university were provided with training on Google Voice Typing (Google Docs) and carried out a series of ASR-based writing tasks over a two-hour period. In order to measure their perceptions of the target criteria, participants filled in a TAM-informed survey consisting of statements about their experience with ASR scored on a 7-point Likert scale. To further explore the participants’ perceptions, semi-structured interviews followed. Findings indicate positive perceptions of ASR’s usefulness in terms of language learning and its ease of use due to the user-friendly voice commands. This suggests that ASR has pedagogical potential, thus requiring further examination to determine its optimal use for L2 writing.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.263 | 0.126 |
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".