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Record W2784162687 · doi:10.5539/elt.v11n2p82

Effects of Sustained Impromptu Speaking and Goal Setting on Public Speaking Competency Development: A Case Study of EFL College Students in Morocco

2018· article· en· W2784162687 on OpenAlexvenueno aff
Latifa El Mortaji

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsImpromptuPublic speakingPsychologyRubricCompetence (human resources)PedagogyMedical educationMathematics educationSocial psychologyLinguistics

Abstract

fetched live from OpenAlex

Research on impact of sustained impromptu speaking on public speaking competency development is scarce and lacking. The researcher investigated Moroccan college students’ public speaking competency development through extemporaneous (i.e. carefully prepared and rehearsed) speech performance, after implementation of a teaching strategy involving treatment through weekly impromptu (i.e., involving little or no preparation) speaking sessions combined with individual goal-setting strategy (teacher feedback). For this purpose, the researcher assessed 64 extemporaneous speeches delivered over the course of a semester using the public speaking competence rubric (PSCR), and observed the students’ public speaking progress through 90 impromptu speaking activities using a weekly goal-setting strategy. Results revealed that a combination of sustained impromptu speaking and goal-setting contributed significantly and effectively to public speaking skills development over the course of the semester. They also clearly showed that the teacher’s weekly goal-setting strategy played a major role in building speakers’ confidence and overall improvement. Considering the linguistic and cultural background of the students involved, together with the speech genres and the instructor’s task requirements, new public speaking competency dimensions and sub-dimensions have been identified.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.373
Teacher spread0.355 · 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 teacher head, 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

Citations10
Published2018
Admission routes1
Has abstractyes

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