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Record W3117320573 · doi:10.23977/aetp.2020.41024

Investigating Pre-service English Teachers' Identity Construction Using Multimodal Discourse Analysis

2020· article· en· W3117320573 on OpenAlexvenueno aff
Zhili Peng, Yongqiang Ye, Yi Wang

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsForegroundingIdentity (music)PedagogyFraming (construction)Construct (python library)Teacher educationDiscourse analysisPsychologyNarrativePre-service teacher educationMathematics educationNarrative inquiryLinguisticsEngineeringComputer science

Abstract

fetched live from OpenAlex

The study of teacher identity has been booming since it was proposed in the last century, and the popularity has not subsided. Some scholars explored teacher identity from the macroscopic perspective using educational narrative and critical discourse analysis, while others explored it with micro-conversational analysis. Among the previous research, few studies focused on the process of pre-service teachers’ identity construction with multimodal discourse analysis. Adopting multimodality design framework for identity construction, this multi-case study of two student teachers examined four dimensions of teacher identity including framing, selection, foregrounding and arrangement. The results indicated a variety of differences in the types of teacher identities which two student teachers constructed. Student teacher A was more inclined to construct authoritative and knowledgeable teacher identity, while student teacher B constructed guiding and amiable teacher identity. It was found that different student teacher identities had different effects on classroom teaching. On the one hand, constructing teachers’ authority too much resulted in less teacher-student interaction and low student participation in the classroom. On the other hand, building an excessively intimate relationship between teacher and students led to poor classroom discipline.

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)0.000
metaresearch head score (Gemma)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.369
Teacher spread0.344 · 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 designTheoretical or conceptual
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

Citations1
Published2020
Admission routes1
Has abstractyes

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