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Record W4220960821 · doi:10.5539/ies.v15n2p41

Pre-Service Teachers’ Self-Efficacy Support Systems Resulting in a Desire to Become Teachers

2022· article· en· W4220960821 on OpenAlexvenueno aff
Nongluck Manowaluilou, Edward M. Reeve

Bibliographic record

VenueInternational Education Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Professional Development and Motivation
Canadian institutionsnot available
FundersKasetsart University
KeywordsPracticumSelf-efficacyPsychologyScale (ratio)Medical educationService (business)Statistical analysisMathematics educationMedicineSocial psychology

Abstract

fetched live from OpenAlex

This study focused on changes in the self-efficacy of 48 men and women pre-service teachers in a Thai computer Teacher Education program. Their self-efficacy levels were measured before and after attending the Pre-service Teacher Support System (PTSS). A shorter version of the Teacher Sense of Efficacy Scale and assessment of the desire to become a teacher were used to collect self-efficacy, PTSS questionnaires, logbooks, and interviews. PTSS is a program developed to assist students with self-efficacy, motivation, and the desire to become teachers. The results show a statistical increase in self-efficacy. The PTSS program designed for pre-service teachers to exchange teaching experiences and techniques they gained during practicum training. The PTSS has resulted in an increase in self-efficacy after attending PTSS sessions with personalized implications, which indicated the support system could help student teachers.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.108
GPT teacher head0.435
Teacher spread0.327 · 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 designObservational
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

Citations7
Published2022
Admission routes1
Has abstractyes

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