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Record W2897346850 · doi:10.26858/publikan.v8i3.5995

Penerapan Model Pembelajaran Discovery Learning Untuk Meningkatkan Hasil Belajar Siswa Pada MataPelajaran IPA Kelas V SDN 124 Batuasang Kecamatan Herlang Kabupaten Bulukumba

2018· article· id· W2897346850 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePublikasi Pendidikan · 2018
Typearticle
Languageid
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMathematics educationAction researchClass (philosophy)PsychologyPedagogyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

T his research is a classroom action research that aims to increase learning outcomes IPA by applying discovery learning model . The approach used in this study is qualitative with the type of research is Class Action Research (PTK) is recycled/ cycles that include planning, execution, observation, and reflection. The data analysis used is qualitative . The results showed that there are increases in learning both on the activities of teachers and students as well as student learning outcomes. From this research can be concluded that teacher teaching activities and student learning activities are increase, student learning outcomes in cycle I not yet in the category enough, in cycle II student learning outcomes have increased are in good category and the application of discovery learning learning model in science subjects can improve the learning outcomes of fifth grade V SDN 124 Batuasang Kecamatan Herlang Kabupaten Bulukumba.

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0050.007
Open science0.0050.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.036
GPT teacher head0.297
Teacher spread0.261 · 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