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Record W4292423759 · doi:10.5539/ies.v15n5p1

The Longer-Term Influences of International Professional Experience on Teachers’ Professional Practice and Growth

2022· article· en· W4292423759 on OpenAlexvenueno aff
Angela Fitzgerald, Rebecca Cooper

Bibliographic record

VenueInternational Education Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyFocus groupProfessional developmentMedical educationFaculty developmentHigher educationFraming (construction)PedagogySociologyMedicine

Abstract

fetched live from OpenAlex

The intention of this paper is to examine the longer-term impacts of international professional experience (IPE). Participants in the study were all early-career teachers who had participated in IPE as part of their education degree and were invited to participate through the alumni office of an education faculty from one university. Thirty participants took part in this research study by completing an online questionnaire with two continuing on to a focus group discussion. The two data sets – from the online questionnaire and interview transcripts – were scrutinized individually using a deductive approach, informed by the five categories from the integrated works of Willard-Holt (2001) and Pence and Macgillivray (2008) as a framing lens. The impact of IPE on the identity formation, practices and career-based decision making of the focus group participants reiterated what emerged from the literature and questionnaire data. This research highlights the impact that IPE has on future teachers’ sense of self and practice as a teacher, their approach to learning, teaching and education more broadly, and on the decisions, they make about their career options and pathways. This study has implications for the ways in which future teachers are prepared for culturally diverse classrooms.

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)0.005
metaresearch head score (Gemma)0.022
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.059
GPT teacher head0.487
Teacher spread0.428 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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