Investigating construct representativeness and linguistic equity of automated oral reading fluency assessment with prosody
Bibliographic record
Abstract
While the automated assessment of oral reading fluency (ORF) using accuracy and speech rate has proliferated, expressiveness of speech, as measured by prosodic features, has been neglected due to its inherent complexity and lack of technological resources. Despite the potential benefits of burgeoning technology for assessing hard-to-measure constructs such as ORF, insensitivity to linguistic diversities threatens valid score interpretations and fair use for all learners. The present study investigated the potential benefits of developing an automated, prosody-inclusive ORF assessment in a post-secondary education setting involving many English language learners (ELLs). The analysis focused on three ways the inclusion of prosody may improve automated ORF assessments: by reducing bias against ELLs, improved prediction of reading comprehension, and improved diagnostic information. Data were analyzed by comparing two different scoring outcomes, the traditional ORF measure and a new prosody-inclusive outcome score, comparing these measures across language background. Results showed that the inclusion of prosody improves automated ORF assessment by reducing discrepancies between ELLs and English first language students caused by automated speech recognition inaccuracies, leads to better prediction of reading comprehension with ELLs, and provides meaningful diagnostic information. Detailed descriptions of the models, their relevance, and implications for the language testing community 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 | 0.049 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".