Exploring English for academic purposes instructors’ perceptions of speech fluency through developing and piloting a rating scale for a paired conversational task
Bibliographic record
Abstract
Much research has explored how perceptions of speech fluency are influenced by a variety of temporal speech features (e.g. speech rate). However, less is known about the influence of non-temporal and conversational speech characteristics on fluency perceptions. To address this gap, this study explored English for Academic Purposes (EAP) instructors' perceptions of speech fluency through developing and piloting a rating scale for a paired conversational task for assessment for learning purposes. A two-phase mixed-methods sequential exploratory design (Creswell, 2009) was used. Seven trained EAP instructors watched videos of seven-minute conversations, elicited from 14 intermediate-to-advanced EAP learners. Afterwards, instructors were audio-recorded discussing their observations about learners' fluency. These recordings were transcribed and coded using in-vivo and pattern coding techniques (Saldaña, 2009). Six themes were identified: efficiency, smoothness, sophistication, clarity, facilitating topics and turns, and supporting the conversation partner. These themes informed the development of a multi-item fluency rating scale. 35 EAP instructors then used the scale to rate eight learners’ speeches. To investigate the construct-relevance of these scale items, a principal component analysis was conducted, producing two components - individual fluency and conversational fluency. Pedagogical activities aligned with the scale are provided.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".