The Future of Text-to-Speech Technology: How Long before it's Just One More Thing we do When Teaching Reading?
Bibliographic record
Abstract
Reading practice is continually under revision, predominantly with the goal of improving reading instruction and ensuring the success of a greater number of readers. As we examine the history of reading research, we encounter countless intervention studies that seek “to prevent and alleviate serious learning disabilities, to increase the number of children who read early and well, and to protect young lives from the unfortunate consequences of failure” (Lyon & Moats, 1997, online document). Over the years, what was once conceived of as intervention for those who struggle has found its way into today's concept of best practice reading instruction. This paper presents a classroom case study where one such intervention, that of text-to-speech technology (TTST) was introduced as day-to-day classroom practice. TTST most often falls under the heading of special education or assistive technology, where its primary purpose is to support students who struggle to read or have a reading disorder that was not preventable nor was it alleviated by traditional interventions. TTST, however, when offered as a choice, allows students to deepen their understanding of reading, how TTST can be used as a reading support, what TTST can and cannot do, and what happens when certain reading skills and strategies break down. Ultimately, when provided with TTST as part of a comprehensive reading approach, students naturally integrated it into the ongoing development of metacognitive strategies, student dialogue and collaboration, spontaneous reader response, and most importantly, self-efficacy and self-advocacy. In the end, students agree that for many, TTST would be a nuisance, for some, a legitimate and equitable choice, and for a few, TTST, will be a lifelong tool. Implications for classroom practice in terms of parent, teacher, and student implementation are discussed.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".