MétaCan
Menu
Back to cohort
Record W2156123198 · doi:10.5539/ass.v4n11p17

Personality and Second Language Learning

2009· article· en· W2156123198 on OpenAlexvenueno aff
Alastair Sharp

Bibliographic record

VenueAsian Social Science · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPersonalityPsychologyLanguage learning strategiesBig Five personality traitsLanguage acquisitionSecond languageTest (biology)Applied psychologySocial psychologyMathematics educationLinguisticsCognition

Abstract

fetched live from OpenAlex

This paper examines the relationships which exist between personality and second language learning and adds to the data available on the use of a highly respected personality indicator, the Myers Briggs Type Indicator (MBTI). Language learning and academic success are both highly correlated with intelligence, but research suggests that the importance of intelligence declines after high school age, partly because of the stronger effects of personality. This study places emphasis on the importance of personality in learning success and examines research evidence on the issue, discussing some of the inconsistent results that have been obtained. A study of 100 Hong Kong university undergraduates was carried out to add to this research base. The instruments used were the MBTI for personality traits, the Strategy Inventory for Language Learning (S.I.L.L.) for learning strategies and a standardized test for language proficiency. Significant statistical relationships were not found and the reasons for this are discussed.

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)0.005
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.020
GPT teacher head0.277
Teacher spread0.257 · 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

Citations53
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAsian Social ScienceSame topicEFL/ESL Teaching and LearningFrench-language works237,207