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Record W2567609816 · doi:10.5539/ies.v10n1p255

How do Teachers Make Sense of Peer Observation Professional Development in an Urban School

2016· article· en· W2567609816 on OpenAlexvenueno aff
Luis Miguel Dos Santos

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAcknowledgementYardstickProfessional developmentPsychologyFaculty developmentMathematics educationPedagogyProcess (computing)Peer feedbackCollegialityComputer science

Abstract

fetched live from OpenAlex

The purpose of the research study is to explore how a peer observation training programme could be beneficial to the professional development of English teachers in an East Asian environment. The research objectives were to improve teaching practice, examine how teachers make sense of the peer observation programme after they have taken part in, and to suggest alternative approaches.Data were collected from three teachers who participated in a peer observation programme at a language school in Hong Kong through an interview process. The research discovered 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 discovered that teachers think that if the peer observation approach is well developed, it can be potentially interesting or generate excitement among teachers. It can support teachers to deliver their possible best practice. There is a general acknowledgement among the participants that there are certain elements of a teacher’s performance that only colleagues in the same or closely-related disciplines can accurately assess. In the absence of a clear cut procedure and requirement for evaluating a person and for the person being evaluated, both parties become frustrated as there is no yardstick of performance. Recommendations for improvement have also been presented.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.127
GPT teacher head0.373
Teacher spread0.247 · 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 designNot applicable
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

Citations28
Published2016
Admission routes1
Has abstractyes

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