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Record W4399893605 · doi:10.5430/wje.v14n2p94

Empowering Teachers' Learning to Foster Innovative Work Behaviors of Students

2024· article· en· W4399893605 on OpenAlexvenueno aff
Watthikorn Phochaito, Wirot Sanrattana, Phrasrivajiravati Phrasrivajiravati

Bibliographic record

VenueWorld Journal of Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationPedagogyWork (physics)Teaching method

Abstract

fetched live from OpenAlex

This research aims to conduct research using the Research and Development (R&D) methodology to obtain an educational innovation called "online self-training program for empowering teachers' learning to foster innovative work behaviors of students" that is effective and can be applied in schools serving as the target population in disseminating research results widely. This online self-training program consists of 2 projects: 1) a teacher development project with seven self-training modules for teacher learning and 2) a teacher project that uses teachers' learning outcomes to develop students, which contains one self-training module to be used as a practice guide for teachers. The results of the experimental research in the first project found that the 14 teachers' post-test scores in the experimental group were in line with the standard criteria of 90/90 and were significantly higher than the scores from the pre-test. Moreover, the results of the experimental research in the second project found that 121 students who were the target group affected by the development had significantly higher scores from the post-test than the pre-test. Since the research results follow the specified research hypotheses, it shows that the educational innovations as the products of this research are of quality and can be disseminated for further use by teachers and students in schools that are the target population.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.386
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.031
GPT teacher head0.437
Teacher spread0.406 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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