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Record W7001052132

HUBUNGAN KEMAMPUAN LITERASI DIGITAL DENGAN KEMAMPUAN MENULIS TEKS BERITA PADA SISWA KELAS VII SMPN 29 MEDAN TAHUN PEMBELAJARAN 2023/2024

2024· other· id· W7001052132 on OpenAlexaff

Bibliographic record

VenueDigital Repository Universitas Negeri Medan (Universitas Negeri Medan) · 2024
Typeother
Languageid
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPurchasing decisionMotivation to learn
DOInot available

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk mengetahui Hubungan Kemampuan Literasi Digital dengan Kemampuan Menulis Teks Berita pada Siswa Kelas VII SMPN 29 Medan Tahun Pembelajaran 2023/2024. Populasi dalam penelitian ini seluruh siswa kelas VII SMPN 29 Medan yang berjumlah 253 orang. Sampel terdiri dari 30 orang yaitu kelas VII-4. Penelitian ini menggunakan metode korelasional dengan menggunakan pendekatan kuantitatif. Instrument yang digunakan dalam penelitian ini adalah tes berbentuk soal pilihan ganda dan essai. Hasil penelitian menunjukkan 1) Kemampuan literasi digital siswa kelas VII SMPN 29 Medan berada pada kategori cukup dengan rata-rata nilai siswa 66,8. 2) Kemampuan menulis teks berita siswa kelas VII SMPN 29 Medan berada pada kategori baik dengan rata-rata nilai 74. 3) Terdapat korelasi atau hubungan yang positif dan signifikan antara kemampuan literasi digital dengan kemampuan menulis teks berita siswa kelas VII SMPN 29 Medan. Hal ini ditunjukkan dari hasil uji t yang telah dilakukan, diperoleh hasil thitung sebesar 7,874 dan ttabel sebesar 1,701. Dari hasil tersebut, terlihat bahwa thitung ttabel maka Ha diterima dan Ho ditolak dengan tingkat hubungan 0,830 yang berarti variabel X memiliki korelasi yang sangat kuat dengan variabel Y.

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)0.001
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), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.442
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0040.005
Science and technology studies0.0020.003
Scholarly communication0.0030.004
Open science0.0040.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0150.009

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 head0.008
GPT teacher head0.203
Teacher spread0.196 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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