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Record W2741545893 · doi:10.5539/ies.v10n8p21

Students’ Personal Initiative towards their Speaking Performance

2017· article· en· W2741545893 on OpenAlexvenueno aff
Nihta V. F. Liando, Raesita Lumettu

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPearson product-moment correlation coefficientMathematics educationEnglish languagePedagogyMathematics

Abstract

fetched live from OpenAlex

This research aims at finding out students’ personal initiative towards their achievement in speaking English. This research was conducted in an English department at a university in North Sulawesi Indonesia. The data were obtained from the sixth semester students in English Language and Literature study program of academic year 2015/2016 consisting of 21 students. In obtaining the data about students’ personal initiative, a questionnaire was distributed, and for the speaking performance, the data were obtained from students’ scores in Public Speaking subject. To find out the relation between these two variables, Pearson’s Product Moment Correlation Coefficient formula was used. The result of this research shows that there is a correlation between students’ personal initiative towards their speaking performance with the value of = (0.52) categorized as a moderate correlation. Based on the findings, it can be concluded that personal initiative of students was important to be considered as one of several determination factors for students’ achievement in English speaking skill. It is suggested that students encourage themselves for taking initiative to speak, and for the teacher to give the students correction and suggestion to help them develop themselves.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
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 head0.204
GPT teacher head0.420
Teacher spread0.216 · 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.

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

Citations15
Published2017
Admission routes1
Has abstractyes

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