Exploring Perceptions of Second Language 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 nontemporal and conversational speech characteristics, as well as listener characteristics such as accent familiarity and conversational style, on fluency perceptions.To address this gap, the present study explored the influence of these characteristics through developing and piloting a fluency rating scale for a paired conversational task for assessment for learning purposes.A twophase mixed-methods sequential exploratory design (Creswell, 2009) provided the methodological framework for the present study.In the first phase, seven trained English for Academic Purposes (EAP) instructors watched videos of seven-minute conversations, elicited from 14 intermediate-to-advanced EAP learners, who performed the conversational task twice with two different partners.Afterwards, instructors were audio-recorded discussing their observations about learners' fluency.These recordings were coded using in-vivo and pattern coding techniques (Saldaña, 2009).Six themes were identified: smoothness, efficiency, sophistication, clarity, facilitating topics and turns, and supporting the conversation partner.These themes informed the development of a multi-item fluency rating scale, used in the second phase of the study.In this phase, a new group of 35 EAP instructors watched four seven-minute video-recorded conversations between eight learners, and then used the scale to rate the performances.Before watching each video, instructors reported their familiarity with students' accents on a six-point scale.Once rating was completed, instructors completed a conversational style questionnaire.The results were as follows.First, a Principal Component Analysis of scale items produced two separate components -individual fluency and conversational fluency.Second, temporal measures of within-clause pause rate correlated significantly with items Chapter One: IntroductionSecond language (L2) speech fluency (i.e.fluency) has long been considered an integral component of L2 speech (Fulcher, 2003).However, fluency has been famously problematic to define, categorize, analyse, and evaluate, as perceptions of this construct may vary widely, even among trained L2 practitioners (Tavakoli & Hunter, 2018;Koponen & Riggenbach, 2000).According to Lennon (1990), in the everyday use of the term, fluency is equated to general oral proficiency, whereas in the realm of language teaching, testing, and learning, the term is defined more narrowly as the overall speed and flow of speech, characterized by its core temporal features (i.e.speed, pauses, and repairs, as per Tavakoli & Skehan's 2005 taxonomy).Yet, "temporal variables are merely the tip of the iceberg as indicators of fluency" (Lennon, 2000, p. 25) as a wide variety of peripheral, non-temporal features such as lexical sophistication, comprehensibility, and conversational features of speech (e.g.turn-taking management) seem to have some degree of influence over how fluency is perceived by L2 practitioners.It would seem then that core temporal measures are inherently integrated with non-temporal peripheral features of the fluency construct, suggesting that temporal measures only constitute one aspect of a larger whole.To date, a fairly substantial amount of research has investigated the effects of a variety of both temporal and non-temporal speech features on influencing perceptions of fluency (e.g.
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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 | 0.010 | 0.022 |
| 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".