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Record W2731131621 · doi:10.5539/ies.v10n7p118

Relationship between Motivation to Achieve and Professional Competence in the Performance of Elementary School Teachers

2017· article· en· W2731131621 on OpenAlexvenueno aff
Mohamad Syarif Sumantri, Prayuningtyas Angger Whardani

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicCompetency Development and Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)School teachersPsychologyMathematics educationAcademic achievementPrimary educationPositive correlationProfessional developmentStudent achievementPedagogySocial psychology

Abstract

fetched live from OpenAlex

This study aims to determine the relationship between achievement motivation and professional competence with regard to the performance of teachers in public elementary schools in Central Java. The quantitative method is used in this study. The results of this study indicate that (1) there is a significant positive relationship between achievement motivation and work performance among elementary school teachers, with a correlation coefficient of 0.961. (2) There is a significant positive relationship between the professional competence and performance of elementary school teachers, with a correlation coefficient of 0.979. (3) There is a significant positive relationship between the achievement motivation and professional competence of teachers (considered together) and the work performance of elementary school teachers, with a correlation coefficient of 0.982 and contribution of 96.4%.

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)0.006
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.180
GPT teacher head0.478
Teacher spread0.298 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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