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Record W2990904593 · doi:10.20319/pijss.2019.53.142152

THE INFLUENCE OF PERSONALITY TYPE ON FOREIGN LANGUAGE LEARNING: A CRITIQUE OF THE ACCELERATIVE INTEGRATED METHOD

2019· article· en· W2990904593 on OpenAlexaboutno aff
Mico Poonoosamy

Bibliographic record

VenuePEOPLE International Journal of Social Sciences · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyType (biology)PersonalityLinguisticsPsychoanalysisPhilosophyGeology

Abstract

fetched live from OpenAlex

The Accelerative Integrated Method (AIM) is an inductive approach to second language acquisition. It was developed in Canada in 1999 by Wendy Maxwell to teach French in primary school. The AIM is currently being used to teach also English, Spanish, Mandarin and Japanese in over 10,000 primary and secondary schools in the world. The method privileges meaning-making and effective communication through a gesture approach. Maxwell claims that the AIM allows learners to reach high levels of communicative proficiency quickly, mainly through an emotional engagement with the language that they learn through dance, drama and creative writing. This paper critically evaluates the AIM; it focuses on how much it draws and impacts on the learner’s personality. It also explores the appropriateness of the AIM for secondary school students who have a higher cognitive developmental age than primary students for whom the AIM was originally designed. The discussion is informed by theories on second language acquisition, emotion and personality type. The paper concludes by making recommendations on learning and teaching methodologies that can challenge and successfully engage foreign language learners while also developing in them intercultural literacy.

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)0.181
metaresearch head score (Gemma)0.278
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.181
Threshold uncertainty score0.958

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1810.278
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.005
Science and technology studies0.0030.021
Scholarly communication0.0090.007
Open science0.0060.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.425
Teacher spread0.394 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venuePEOPLE International Journal of Social SciencesSame topicEducation and Critical Thinking DevelopmentFrench-language works237,207