Analisis Kesiapan Pengguna Lective Menggunakan Metode Technology Readiness Index (TRI)
Bibliographic record
Abstract
Lective merupakan sarana pembelajaran online berbasis website yang dirancang bagi dosen untuk mempermudah dalam mendesain Rencana Pembelajaran Semester (RPS) dan Rencana Pelaksanaan Pembelajaran (RPP).dan sistem ini sangat membantu bagi dosen-dosen yang tidak memiliki latar belakang pendidikan maka tools ini sangat membantu bagi mereka. Lective ini masih bisa dikatakan baru dan masih dalam proses pengembangan. kurangnya penerapan Lective di lingkungan Universitas Muhammadiyah Malang merupakan salah satu faktor kendala dalam pengembangannya dikarenakan kesiapan dari penggunanya yaitu dosen yang masih kurang memahami secara penuh tentang penggunaan Lective. Technology Readiness Index (TRI) merupakan indeks untuk mengukur kesiapan pengguna terhadap teknologi baru. TRI menggunakan serangkaian pernyataan kepercayaan/keyakinan dalam melakukan survei untuk mengukur secara menyeluruh tingkat kesiapan teknologi dari individu yang meliputi Optismism, Innovativeness,Discomfort dan Insecurity, Penelitian diawali dengan penyusunan instrumen penelitian, penyebaran kuisioner, dan analisis hasil kuisioner. Berdasarkan hasil analisis, penelitian ini menghasilkan sebuah pernyataan dimana Optimism, Innovativeness, Discomfort dan Insecurity tidak mempengaruhi kesiapan pengguna Lective. Dikarenakan hasil yang didapat berpengaruh negatif terhadap kesiapan pengguna sistem Lective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".