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The Correlation Between Saudi EFL Students Motivational and Attitudinal Behaviors and their Performance and Academic Achievement in English Language

2017· article· en· W2767664748 on OpenAlexaboutno aff
Ayedh Dhawi Mohammed Al-Mohanna

Bibliographic record

VenueInternational Journal of Asian Social Science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyEnglish languageMathematics educationPositive correlationPoint (geometry)Language acquisitionCorrelationAcademic achievement

Abstract

fetched live from OpenAlex

Research demonstrates that language learners' motivation and attitudes are a standout amongst the most imperative factors that can impact the success or failure in learning that language. Along these lines, the principle point of the present study is to investigate Saudi EFL learners’ motivation for learning English and their attitudes toward learning the language and toward its native speakers. It also looks at the correlation between their motivational and attitudinal behavior and their performance and academic achievement in English language. In addition, it makes some pedagogical implications, based on the findings. A questionnaire and an interview were developed and used to collect data from participants. A total of sixty Saudi students, who were enrolled in intensive English courses at different universities in Canada, participated in the questionnaire, and thirty (half of the specimen) of them were arbitrarily chosen to partake in a subsequent meeting. The aftereffects of the survey and the meeting demonstrated that most of the Saudi EFL understudies had high inspiration to learn English, uplifting states of mind toward learning English and inspirational dispositions toward the local English speakers. They also revealed that there was a clear positive correlation between Saudi EFL students’ motivational and attitudinal behavior and their performance and academic accomplishment in the language.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.143
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.340
Teacher spread0.309 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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