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Record W4306698124 · doi:10.24071/llt.v25i2.4432

EXPLORING THE LEVEL OF STUDENTS’ SELF-EFFICACY IN SPEAKING CLASS

2022· article· en· W4306698124 on OpenAlexaff
Efrika Siboro, Antonius Setyawan Sugeng Nur Agung, Charito A Quinones

Bibliographic record

VenueLLT Journal A Journal on Language and Language Teaching · 2022
Typearticle
Languageen
FieldPsychology
TopicLearning Styles and Cognitive Differences
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsGeneralityDimension (graph theory)PsychologySelf-efficacyClass (philosophy)Mathematics educationSocial psychologyMathematicsComputer science

Abstract

fetched live from OpenAlex

Exploring the level of the students’ self-efficacy toward their speaking ability is the grand design of this study. The participants of this study were 28 non-native students from the suburban area in West Borneo. Those students belong to the third semester of the speaking class. In collecting the data, they were given a questionnaire. An in-depth interview was also conducted with 3 prominent students to validate and triangulate the represented data in the questionnaire result. Adopting Bandura’s theory, the results of this study show that the students manifested slightly high self-efficacy in the magnitude dimension, slightly high self-efficacy in the generality dimension, and very high self-efficacy in the strength dimension. In addition, the in-depth interview affirms that the students’ level in magnitude is influenced by their educational background; the students’ level in generality is affected by their interests in their particular field, and the student's level of strength is determined by their strong belief.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.097
GPT teacher head0.364
Teacher spread0.267 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueLLT Journal A Journal on Language and Language TeachingSame topicLearning Styles and Cognitive DifferencesFrench-language works237,207