Bibliographic record
Abstract
Inovasi teknologi memberikan kemungkinan baru untuk mengubah proses pengajaran dan pembelajaran. Kemudahan user dan manfaat yang dirasakan secara nyata oleh user menjadi faktor yang mempengaruhi sebuah mobile learning digunakan atau tidak. Kebiasaan pelajar dalam belajar antara lain mencari contoh soal dan pembahasan materi, definisi dari istilah-istilah tertentu dan materi yang lebih lengkap. Penelitian ini berfokus pada penyajian materi yang memudahkan user untuk menelusuri materi. Metodologi yang dipakai yaitu eksperimen dan pengisian kuesioner oleh mahasiswa Prodi Teknik Komputer dan Jaringan pada mata kuliah Jaringan Komputer. Data dari responden dievaluasi menggunakan konsep Technology Acceptance Model (TAM) dan Sructural Equation Modeling. Luaran yang akan dicapai berupa Publikasi Ilmiah. Pada penelitian ini juga akan dilakukan validasi modul dan/atau subsistem dalam lingkungan yang relevan yakni pada prodi Teknik Komputer Dan Jaringan khusus pada mata kuliah Jaringan Komputer. Implementasi prototipe yang sesuai dengan lingkungan/antarmuka.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".