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Record W2546680615 · doi:10.5539/elt.v9n12p22

Self-Regulated Strategy Instruction for Developing Speaking Proficiency and Reducing Speaking Anxiety of Egyptian University Students

2016· article· en· W2546680615 on OpenAlexvenueno aff
Samah Mohammed Fahim El-Sakka

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAnxietyLanguage proficiencySignificant differenceTest anxietyTest (biology)Mathematics educationClinical psychology

Abstract

fetched live from OpenAlex

<p>The aim of this study was to investigate the effect of teaching some self-regulated strategies to Egyptian university students on improving their speaking proficiency and reducing their speaking anxiety. The design of the study was a one group pre-posttest quasi experimental design. Forty 3<sup>rd</sup>- year EFL university students were selected to form the experimental group of the study. This experimental group was tested using the pre-post speaking test and speaking anxiety scale before and after being exposed to the self-regulated strategy treatment. Paired- samples t-test revealed significant differences between the pretest and posttest of speaking proficiency as well as speaking anxiety in favor of the posttests. Additionally, a negative correlation was shown between speaking proficiency and speaking anxiety. It was concluded that teaching self-regulated strategies to university students was effective in developing their speaking proficiency and reducing their anxiety considerably.</p>

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.021
GPT teacher head0.335
Teacher spread0.314 · 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 designObservational
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

Citations44
Published2016
Admission routes1
Has abstractyes

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