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Record W4320009702 · doi:10.17227/folios.57-12985

Pre-service Teachers’ Experiences in Constructing and Redefining Conceptions of Language Teaching

2023· article· en· W4320009702 on OpenAlexaff
Wilson Hernández Varona, Diego Fernando Macías

Bibliographic record

VenueFolios · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsConcordia University
Fundersnot available
KeywordsPracticumNarrativeVocational educationPedagogyPsychologyTeacher educationMathematics educationLanguage educationProcess (computing)Computer science

Abstract

fetched live from OpenAlex

Beginning teachers are often left on their own to endure life at school perhaps as a result of the assumption that learning to teach comes from the experience of teaching, or that the theoretical knowledge gained in teacher education programs is sufficient to deal with such an endeavor. This narrative study investigated student teachers’ retrospective conceptions of English language teaching as they entered a teacher education program and as they completed their practicum at a public school, to better understand their process of learning to teach English. By analyzing participants’ reported experiences, we identified a growing awareness of the contextual circumstances regarding students’ needs and social realities which led to a confrontation of the theoretical insights’ participants had gained through the teacher education program. Findings also revealed participants’ overall dissatisfaction with the learning experiences during their English lessons in public schools and a rather positive view of their learning experiences in language institutes and in English vocational education and training courses.

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)0.008
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.014
Scholarly communication0.0070.005
Open science0.0010.006
Research integrity0.0020.006
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 head0.041
GPT teacher head0.292
Teacher spread0.251 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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