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Record W2558495361 · doi:10.5539/ies.v9n12p219

Responding to the Need for Re-Conceptualizing Second Language Teacher Education: The Potential of a Sociocultural Perspective

2016· article· en· W2558495361 on OpenAlexvenueno aff
Minh Hue Nguyen

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsConceptualizationSociocultural evolutionSociocultural perspectivePerspective (graphical)EpistemologySociologyPsychologyField (mathematics)PedagogyLinguisticsComputer science

Abstract

fetched live from OpenAlex

<p class="apa">This paper aims to engage with and respond to recent calls in the literature for a unifying theoretical framework to understand second language teacher education (SLTE). It critically reviews the major conceptualizations of SLTE in relation to the key conceptualizations of second language (L2) teaching. The review identifies shortcomings in traditional perspectives on L2 teaching and SLTE and the need to re-conceptualize SLTE as a field. A recent re-conceptualization of SLTE is seen through the shift towards a social constructivist perspective, a redefinition of the knowledge base, research that responds to the epistemological shift, and a sociocultural perspective on SLTE. The existing literature shows that although there is now a growing body of research that looks into the various dimensions of SLTE, few studies have gained a comprehensive and systematic view of the complexities of SLTE. The paper argues that a sociocultural perspective, especially a combination of Vygotsky’s genetic method and Engeström’s proposal of the third generation of activity theory, has become a powerful way of understanding L2 teacher learning, which corresponds to the need for a re-conceptualization of SLTE. This paper calls for more research using a sociocultural framework to enrich its knowledge base.</p>

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0010.000

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 head0.078
GPT teacher head0.494
Teacher spread0.416 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2016
Admission routes1
Has abstractyes

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