APLIKASI PEMBELAJARAN AUDIT SISTEM INFORMASI BERBASIS MOBILE QUIZIZZ
Bibliographic record
Abstract
Penelitian ini menggunakan pendekatan kuantitatif dengan desain eksperimen. Penelitian ini bertujuan untuk mengevaluasi efektivitas penggunaan Quizizz dalam meningkatkan pemahaman mahasiswa terhadap materi audit sistem informasi. Teknik sampling yang digunakan untuk menentukan sampel pada penelitian ini adalah teknik cluster random sampling. Hasil analisis data akan diinterpretasikan untuk menentukan efektivitas penggunaan Quizizz dalam pembelajaran audit sistem informasi. Diskusi mengenai temuan ini akan mencakup implikasi untuk pengajaran dan rekomendasi untuk penelitian lebih lanjut. Dalam era digital yang terus berkembang, pendidikan tinggi, khususnya dalam bidang sistem informasi, menghadapi tantangan untuk menyediakan metode pembelajaran yang efektif dan menarik. Aplikasi Quizizz dapat dijadikan salah satu media pembelajaran yang kreatif, inovatif dan menyenangkan bagi mahasiswa yang sedang mempelajari mata kuliah audit sistem informasi.
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 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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".