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Record W3158518707 · doi:10.22215/etd/2020-14086

Exploring Perceptions of Second Language Speech Fluency Through Developing and Piloting a Rating Scale for a Paired Conversational Task

2020· dissertation· en· W3158518707 on OpenAlexafffund
Kent Williams

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsCarleton University
FundersBrock UniversityUniversity of Waterloo
KeywordsFluencyPsychologyCLARITYConversationRating scaleTask (project management)PerceptionScale (ratio)Mathematics educationCommunicationDevelopmental psychology

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.107
GPT teacher head0.292
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes2
Has abstractyes

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