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Record W2516586907 · doi:10.5539/elt.v9n10p39

Foreign Language Teachers’ Professional Development through Peer Observation Programme

2016· article· en· W2516586907 on OpenAlexvenueno aff
Luis Miguel Dos Santos

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyProfessional developmentPeer feedbackMathematics educationPedagogyPerceptionPeer reviewForeign languageProcess (computing)English as a foreign languageMedical educationPolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

<p>The<strong> </strong>purpose of the research is to explore the development of peer-observation programme for the use of an extension language school in Hong Kong. The research objectives were to explore teachers’ perceptions on a peer observation programme as a means to improve teaching practice, examine how teachers make sense of the peer observation programme after they have taken part in it and to suggest alternative approaches and measures by which schools can improve peer observation programmes in schools.</p><p>Data was collected from six teachers who participated in peer observation programme in Hong Kong through an interview process. The research has found out that peer observation can be a good tool for continuous professional development for teachers in order to develop their teaching strategies. This is especially important within the field of language education. From the analysis, most teachers are wary of the practicalities of peer observation due to the sensitivity that is associated with it. The research also found out that teachers think that if the peer observation approach is well developed, it can be potentially interesting or generate excitement among teachers.</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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.278
Teacher spread0.237 · 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.

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

Citations26
Published2016
Admission routes1
Has abstractyes

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