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Record W3022622034 · doi:10.26740/jdmp.v4n2.p113-121

Supervisi Akademik Meningkatkan Kompetensi Pedagogik Guru

2020· article· en· W3022622034 on OpenAlexaff
Heri Mujiono

Bibliographic record

VenueJDMP (Jurnal Dinamika Manajemen Pendidikan) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Leadership and Teacher Performance
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsDocumentationAction researchCompetence (human resources)Data collectionMathematics educationPsychologyAcademic yearMedical educationPedagogyComputer scienceMathematicsMedicineSocial psychologyStatistics

Abstract

fetched live from OpenAlex

The condition of the teacher at SDN Kepanjen 2 shows far from ideal conditions. The results of preliminary surveys conducted by researchers in the execution of teachers ' duties, seven aspects of pedagogic competence teachers have not seen showing the skills of teachers in carrying out learning. Academic supervision is a selected action researcher to help teachers develop pedagogic skills of the teacher. This research is done cycle by cycle according to the concept of research action, planned there are 2 cycles with each cycle implemented measures planning, implementation, observation, reflection. Data collection techniques used in research using observations, interviews and documentation studies. The results of data analysis obtained exposure that proves that of the seven competencies that are targeted to research obtained an average value increase of 86%. So it can be concluded that the application of academic supervision can improve the pedagogic competence of 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0190.003

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 head0.133
GPT teacher head0.340
Teacher spread0.207 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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