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Record W4405955961 · doi:10.5539/elt.v18n1p64

Dynamically Stable Evolution of Ideal Selves in Motivation of High School English Teachers in China

2025· article· en· W4405955961 on OpenAlexvenueno aff
Xueshan Zhang

Bibliographic record

VenueEnglish Language Teaching · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyIdeal (ethics)ChinaMathematics educationPedagogySocial psychologyEpistemology

Abstract

fetched live from OpenAlex

Given that few studies focus on the complex and dynamic nature of English teachers’ teaching motivation in China (Dörnyei & Ushioda, 2021), this study aims to explore English teachers’ teaching motivation development with the possible selves theory (Markus & Nurius, 1986), which offers the most comprehensive and versatile lens for the analysis of teachers’ teaching motivation. There were seven participants in this study, including English teachers at early, mid, and late stages of their careers from a public high school in northern China. Data were collected through semi-structured interviews and teachers’ reflective journals. This study found that there was a dynamically stable evolution of participants’ ideal teacher selves. While the dynamics meant that there was continuous emergence of new ideal images, the stability meant that teachers’ ideal selves were static within a particular period. It is also true that the emergence of new ideal images did not mean that participants had discarded or fully realized prior ones. On the contrary, they preserved and adapted key components of previous ideal images while acquiring new ones. In effect, they formed a synthesized ideal teacher self with various components (i.e., key components of various ideal images emerging at different professional stages), which is susceptible to future changes. In addition, the agreement between ideal selves and the ought-to self enhanced participants’ motivation.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.279
Teacher spread0.273 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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