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Record W3097666803 · doi:10.21043/quality.v8i2.8097

Supervisi Akademik Kepala Madrasah Dalam Meningkatkan Kompetensi Profesional Guru (Studi Multi Kasus Madrasah Aliyah Negeri Kabupaten Pati)

2020· article· id· W3097666803 on OpenAlexaff
Faizatun Faizatun, Fathul Mufid

Bibliographic record

VenueQUALITY · 2020
Typearticle
Languageid
FieldSocial Sciences
TopicSchool Leadership and Teacher Performance
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsSupervisorPsychologyPedagogyHumanitiesManagementPhilosophy

Abstract

fetched live from OpenAlex

Penelitian ini dilatarbelakangi kurang optimalnya kegiatan Kepala Madrasah dalam mensupervisi guru. Penelitian ini bertujuan untuk menjawab permasalahan: (1) bagaimana pelaksanaan supervisi akademik oleh Kepala Madrasah, (2) bagaimana faktor pendukung kegiatan Supervisi Akademik dan hambatannya, serta (3) bagaimana peran Kepala Madrasah sebagai supervisor. Penelitian ini mengikuti model studi multi kasus, dengan pendekatan kualitatif deskriptif. Berlokasi di MAN 1 Pati dan MAN 2 Pati. Hasil penelitian menunjukkan: (1) pelaksanaan Supervisi Akademik meliputi tiga kegiatan, yaitu perencanaan program (b) strategi Supervisi Akademik, (c) evaluasi menggunakan instrumen penilaian baku mengacu aturan Kementerian Agama, dengan tindak lanjut memberikan penguatan dan penghargaan serta pembinaan dan pelatihan. (2) Faktor-faktor pendukung meliputi jadwal supervisi akademik, kesediaan guru disupervisi, sikap guru senior yang kooperatif, kelengkapan administrasi pembelajaran, kontinyuitas pelaksanakan Supervisi Akademik. Faktor-faktor penghambat terdiri dari keterbatasan waktu Kepala Madrasah dan guru, beban guru mengajar yang tidak sesuai dengan latar belakang Pendidikan (3) Peran Kepala Madrasah sebagai Supervisor memiliki kompetensi: merencanakan program, melaksanakan dan melakukan evaluasi serta menindaklanjuti hasil Supervisi Akademik. This research is motivated by the less optimal activities of the Madrasah Head in supervising teachers. This study aims to answer the problem:(1) how the implementation of academic supervision by the Head of Madrasa (2) how the factors supporting Academic Supervision activities and their obstacles, and (3) how the role of the Madrasah Head as a supervisor. This research follows a multi-case study model, with a descriptive qualitative approach. Located in MAN 1 Pati and MAN 2 Pati. The results showed: (1) Implementation of Academic Supervision includes three activities, namely: (a) program planning, (b) Academic Supervision strategy, and (c) evaluation using standard assessment instruments referring to the rules of the Ministry of Religion, with follow-up giving reinforcement and appreciation as well as coaching and training. (2) Supporting factors include the schedule of academic supervision, the willingness of supervised teachers, fellow teaching professions, the attitude of the cooperative senior teachers, completeness of the administration of learning, continuity of the Madrasah Head in carrying out Academic Supervision. The inhibiting factors consist of the limited time of the Madrasah Head and the teacher, the burden of the teacher teaching that is not following the educational background. (3) The Role of the Madrasah Head as a Supervisor has competence: planning programs, implementing and evaluating, and following up on Academic Supervision results. Implementation of Academic Supervision includes three activities, namely: (a) program planning, (b) Academic Supervision strategy by setting targets focused (c) evaluation using standard assessment instruments. (2) Supporting factors. (3) The Role of the Madrasah Head as a Supervisor has competence.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.002

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 head0.237
GPT teacher head0.386
Teacher spread0.149 · 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 designQualitative
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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Citations12
Published2020
Admission routes1
Has abstractyes

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