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Record W2606474678 · doi:10.5539/elt.v10n5p81

The Indonesian EFL Learners’ Motivation in Reading

2017· article· en· W2606474678 on OpenAlexvenueno aff
Hairus Salikin, Saidna Zulfiqar Bin-Tahir, Reni Kusumaningputri, Dian Puji Yuliandari

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyDeci-IndonesianReading (process)Reading motivationIntrinsic motivationClass (philosophy)Mathematics educationSelf-determination theoryMotivation theoryGoal theoryPedagogyLinguisticsSocial psychologyAutonomyComputer science

Abstract

fetched live from OpenAlex

The motivation will drive the EFL learners to be successful in reading. This study examined the Indonesian EFL learners’ motivation in reading activity based on Deci and Ryans’ theory of motivation including intrinsic and extrinsic. This study employed mixed-method design. The data obtained by distributing questionnaire and arranging the group interviewed. The subject of the study involved 42 freshmen students of English department, the faculty of humanities at Jember University in the academic year 2015-2016. The results found that both intrinsic and extrinsic motivations have significant contribution in motivating the learners to read the English text. The intrinsic motivation played the important role in students’ reading activities. Besides, the extrinsic motivation found the teacher’s role as the learners’ motivator in reading the English text through their method implemented in the reading class.

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)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.022
GPT teacher head0.320
Teacher spread0.298 · 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

Citations60
Published2017
Admission routes1
Has abstractyes

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