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Record W2775592029 · doi:10.17507/tpls.0712.01

The Coupling of Second Language Learning Motivation and Achievement According to Gender

2017· article· en· W2775592029 on OpenAlexaff
Callie Mady, Alexandra Seiling

Bibliographic record

VenueTheory and Practice in Language Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsNipissing University
Fundersnot available
KeywordsPsychologyReading (process)Test (biology)Second languageRegression analysisSocial psychologyDevelopmental psychologyMathematics educationLinguistics

Abstract

fetched live from OpenAlex

Despite research investigating gender differences in second language motivation, the examination of such differences with a coupling of motivation and achievement evidence is less common. Given that increased motivation is a contributory factor of achievement (e.g., Schmidt et al., 2001) where gender can also be an influencing variable, it is important to examine the influence of gender on motivation and proficiency in second language education. The following article explores the motivation of 87 Grade 6 early French immersion students through the means of a questionnaire, grounded in Gardner’s socio-educational (1985) and MacIntyre’s (1994) willingness to communicate models. Through multiple regression analysis, the questionnaire findings were examined to see, which if any, variables predicted French proficiency as measured through a multi-skills French test. The female participants outperformed the males in French reading, writing and speaking, whereas only one significant difference was found on the questionnaire items (i.e., language awareness). Although the multiple regression analyses showed both increases and decreases in French achievement according to questionnaire items, where there were significant differences being female was associated with increases in French achievement. These findings offer a gateway to further research, as educators strive to offer quality second language education to all.

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)0.004
metaresearch head score (Gemma)0.008
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.067
GPT teacher head0.367
Teacher spread0.301 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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