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Record W4388951407 · doi:10.31571/gervasi.v7i1.4448

Pelatihan Penyusunan RPP IPA Blended Learning Berbasis TPACK Sebagai Upaya Guru dalam Menghadapi Pembelajaran Pasca Covid-19

2023· article· id· W4388951407 on OpenAlexaff
Mega Elvianasti, Novia Lestari, Maesaroh Maesaroh, Irdalisa Irdalisa, Husnin Nahry Yarza, Rikizaputra Rikizaputra

Bibliographic record

VenueGERVASI Jurnal Pengabdian kepada Masyarakat · 2023
Typearticle
Languageid
FieldSocial Sciences
TopicEducational Curriculum and Learning Methods
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesBlended learningPhysicsArtMathematics educationPsychologyEducational technology

Abstract

fetched live from OpenAlex

Pengabdian ini dilaksanakan dengan tujuan memberikan pelatihan bagi guru dalam menyusun Rencana Pelaksanaan Pembelajaran (RPP) blended Learning dan berbasis Technological Pedagogical Content Knowledge (TPACK) sehingga guru dapat melaksanakan pembelajaran sesuai dengan protokol kesehatan pascapandemi Covid-19. Pasca Covid-19 guru diharapkan dapat merancang dan melaksanakan pembelajaran blended learning. Sebagai penunjang terlaksananya blended learning, guru diberikan pelatihan penyusunan RPP berbasis TPACK. Uraian kegiatan pelatihan tersebut yaitu guru diberikan materi membuat RPP merdeka dan blended learning, serta evaluasi secara online dilanjutkan dengan materi RPP berbasis TPACK. Hari kedua, guru diberikan pelatihan bagaimana menyusun RPP Ilmu Pengetahuan Alam (IPA) blended learning dan berbasis TPACK sesuai dengan kondisi dan situasi sekolah, hari ketiga dilaksanakan evaluasi terhadap RPP yang sudah dibuat. Hasil yang dicapai  adalah guru memahami materi yang disampaikan dengan baik dan dapat mengimplementasikan materi dan waktu penyampaian materi yang sudah baik sekali.

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)0.012
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.006
Science and technology studies0.0080.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0080.005

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 head0.063
GPT teacher head0.372
Teacher spread0.309 · 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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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