The Use of Automated Speech-to-Text Captioning to Support Linguistically Diverse Students and Students who are Deaf or Hard of Hearing
Bibliographic record
Abstract
There is extensive research literature on the use of captioning to support learning for both students with hearing loss and for English Language Learners (ELLs), based on Fletcher & Tobias' (2005) Multimedia Principle. Although video is used extensively in English Language Teaching, use of this strategy by teachers has been limited due to technological constraints. However, advances in automated captioning now provide simple, accessible and cost-effective use. This paper is an integrative literature review (Belyea & Nicholl, 1998; Torraco, 2005) exploring the intersection of efficacy of captioning for ELLs, and accuracy of current automated captioning platforms to answer the question, "Does automated captioning technology provide an effective strategy for English Language Teachers to use in day-to-day teaching?” Results indicated that while automated captioning is not currently accurate enough to be recommended for face-to-face teaching, it can be used to make captioning accessible for pre-recorded learning materials.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".