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MENINGKATKAN KOMPETENSI GURU DALAM MELAKSANAKAN PROSES PEMBELAJARAN MELALUI SUPERVISI AKADEMIK BERBASIS ICT DI KABUPATEN MADIUN

2023· article· id· W4409599107 on OpenAlexaff

Bibliographic record

VenueEl-Wasathiya Jurnal Studi Agama · 2023
Typearticle
Languageid
FieldSocial Sciences
TopicSchool Leadership and Teacher Performance
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsInformation and Communications TechnologySociologyMathematics educationComputer sciencePsychologyWorld Wide Web

Abstract

fetched live from OpenAlex

ABSTRAK Penelitian ini bertujuan untuk Meningkatkan kemampuan dan keterampilan guru dalam menyusun perencanaan pembelajaran dan mengelola kegiatan proses pembelajaran. Meningkatkan kemampuan Guru dalam memanfaatkan Information and Communications Technology dalam menunjang proses belajar mengajarnya di kelas. Menumbuhkan persesi positif guru terhadap pelaksanaan supervisi akademik yang dilakukan oleh kepala sekolah dan pengawas sebagai layanan bantuan proses pembelajaran dalam meningkatkan mutu pendidikan.Meningkatkan intensitas kegiatan Musyawarah Guru Mata Pelajaran sebagai wahana peningkatan kemampuan profesionalisme Guru. Penelitian ini menggunakan metode School Action Reserch (SAR), penelitian ini mengacu model PTS yang dikembangkan oleh Kemmis dan Mc Taggart (2008) dalam Ritawati yang terdiri dari 4 tahap yang meliputi dengan Perencanaan, Tindakan , Observasi dan Refleksi. Subjek Penelitian adalah Guru di SMK Negeri 1 Gemarang dan SMK Negeri 1 Wonoasri yang berjumlah 199 orang. Hasil penelitian menunjukkan bahwa dari rata-rata tingkat kemampuan guru pada siklus I sebesar 65,05% yang tergolong kurang, dan meningkat pada siklus II menjadi 72,14% yang tergolong kurang dan pada siklus III meningkat menjadi 86,70% dengan kategori baik Kata Kunci: Kompetensi Guru, Supervisi Akademik, ICT

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.005
Science and technology studies0.0050.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.004

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 head0.064
GPT teacher head0.322
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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