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Record W2573890206 · doi:10.5539/ass.v13n2p159

Teachers’ Experiences of Collaborating in School Teaching Teams

2017· article· en· W2573890206 on OpenAlexvenueno aff
Shih-Hsiung Liu, Hsien‐Chang Tsai

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
FundersMinistry of Science and Technology, Taiwan
KeywordsCurriculumFocus groupPedagogyPsychologyMedical educationSociologyMedicine

Abstract

fetched live from OpenAlex

Teachers in numerous countries worldwide often confront education reforms in their career, in which, collaborating is considered a feasible approach to changing teachers’ traditional teaching philosophy. This study aims to examine Taiwanese teachers’ experiences of collaborating in school teaching teams. We invited six teachers from different schools for an interview. Afterward, we conducted two sessions of focus-group interviews with 18 participants from various roles in teaching teams as well as various geographical areas. The findings show that information exchanges of education works, uncoordinated processes of collaboration, and discussions not involving pedagogical knowledge are the general experiences on participating in the teaching teams. Certain barriers to teacher collaborations are from inadequate focuses during team discussions and a lack of curriculum leadership. Through experience-sharing, the participants considered that a focus on student learning during discussions and examples of practices for curriculum leadership were the key aspects for successful experiences in teacher collaborations.

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)0.008
metaresearch head score (Gemma)0.019
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0140.007
Scholarly communication0.0060.004
Open science0.0020.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.078
GPT teacher head0.431
Teacher spread0.353 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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