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Record W2373891184 · doi:10.21083/nrsc.v0i9.3676

“Rational, Emotional, Affective Learning” and the Use of Innovative Methods in Foreign Language Teaching

2016· article· en· W2373891184 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueNouvelle Revue Synergies Canada · 2016
Typearticle
Languageen
FieldComputer Science
TopicLinguistic Studies and Language Acquisition
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsForeign languageCognitionCompetence (human resources)Foreign language teachingLanguage educationLinguistic competenceMathematics educationLanguage acquisitionPsychologyLinguisticsSociologySocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

Foreign language didactics is a field which, notwithstanding the many centuries of application and development, is still very methodologically problematic. Statistics clearly show that, in certain countries, the educational system provides foreign language instruction that is not apt and does not deliver effective linguistic competence; a few methods, however, have been developed to contravene this problem. In the specific case of Italy, a country which is renowned for its general monolingualism, Associazione Culturale Linguistica Educational (ACLE) has developed an innovative language teaching method (Rational, Emotional, Affective Learning) to attempt to fill the lacunae of the Italian school system, especially in regards to the teaching of English as a foreign language. This paper seeks to briefly outline the REAL method and its applications, describe its affinity to didactic and cognitive theories, and speculate on its potential effectiveness.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.713
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.274
Teacher spread0.258 · 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