MétaCan
Menu
Back to cohort
Record W2182717644 · doi:10.31686/ijier.vol2.iss11.264

Analysis of UPSI Teacher Clinical Experience In Comparative Perspective and Suggestion for New Teacher Clinical Experience Structure

2014· article· en· W2182717644 on OpenAlexaboutno aff
Wong Yeou Min, Mohd Hassan Abdullah, Rosnidar Mansor, Syakirah Samsudin

Bibliographic record

VenueInternational Journal for Innovation Education and Research · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsOpen access journalPerspective (graphical)Duration (music)PsychologyTeacher educationMedical educationClinical supervisionSociologyPedagogyMedicinePolitical scienceMathematicsLawMEDLINE

Abstract

fetched live from OpenAlex

This study was conducted to compare and discuss the teacher clinical experience structure offered by the Sultan Idris Education University (UPSI) and the chosen universities from Singapore, Hong Kong, Canada, United States of America, United Kingdom, and Australia. This comparative analysis is carried out using a qualitative approach which will focus on the purposes, duration, timing and phases or components of the teacher clinical experience offered by the UPSI and the chosen universities. This analysis has identified that the time allocation for the teacher clinical experience of the UPSI was too short; the timing for pre-service teachers to undergo teacher clinical experience was inappropriate, and the phases or components of teacher clinical experience adopted was insufficient. This paper will suggest a new teacher clinical experience structure and provide implications that can be learnt by the UPSI from other universities abroad to enhance its existing teacher clinical experience.

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)0.010
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 score0.005
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.285
GPT teacher head0.644
Teacher spread0.359 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal for Innovation Education and ResearchSame topicCollaborative Teaching and InclusionFrench-language works237,207