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Record W7084124967 · doi:10.30651/aks.v8i2.25576

Model Pembelajaran Skenario Pengalaman Multi Peran dalam Mencegah Bullying di SMP Muhammadiyah Malang

2025· article· id· W7084124967 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageid
FieldSocial Sciences
TopicInformation Technology and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMotivation to learnResearch method

Abstract

fetched live from OpenAlex

Perilaku bullying masih banyak dilakukan siswa di SMP Muhammadiyah Malang terutama bullying secara verbal antara lain dengan mengolok-olok, mencaci maki, mengumpat, dan menghina. Dampak dari bullying verbal ini korban menjadi malu, minder, menghindar dari pergaulan, dan menjadi malas sekolah. Pihak sekolah sering melakukan edukasi pada siswa siswi dengan tujuan untuk tidak melakukan bullying lagi dan melapor ke sekolah jika ada yang dibully. Akan tetapi perilaku bullying masih sering dilakukan oleh siswa siswa. Tujuan dari kegiatan ini untuk meningkatkan empati pada siswa siswi dengan demikian mampu membantu teman yang dibully. Metodenya adalah edukasi dan model pembelajaran skenario berbasis pengalaman multi peran pada kelas VII, VIII, dan IX dengan mengukur skor empati sebelum dan sesudah kegiatan menggunakan the Toronto empathy questionnaire (TEQ), di mana siswa siswi akan bermain peran bergantian baik sebagai korban, pelaku, maupun pengamat dengan skenario yang sudah dibuat. Pada siswa kelas 7, dari 66 siswa, sebelum kegiatan skor empati rendah 37 siswa dan sesudah kegiatan 32 siswa. Siswa kelas 8 dari 47 siswa, sebelum kegiatan skor empati rendah 26 siswa dan sesudah kegiatan 23 siswa. Siswa kelas 9,sebelum kegiatan skor empati rendah 45 siswa dan sesudah kegiatan 37 siswa. Kesimpulan : Edukasi dan model pembelajaran skenario berbasis pengalaman multi peran dapat diterapkan untuk meningkatkan empati pada siswa SMP dalam mencegah bullying.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0040.001
Scholarly communication0.0030.006
Open science0.0060.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.000

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 head0.233
GPT teacher head0.551
Teacher spread0.318 · 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.

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
Published2025
Admission routes1
Has abstractyes

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