E99: Desktop Game Application for Learning Asmaul Husna
Bibliographic record
Abstract
Nowadays, students still use conventional methods to learn about the Asmaul Husna, the use of technology has not been fully utilized. This causes difficulty in understanding of students in the learning process. Moreover, causing boredom in memorizing Asmaul Husna. Need appropriate learning media needed so students become more interested in memorizing the Asmaul Husna. Therefore, the purpose of this study is to design and develop the game to introduce Asmaul Husna in the form of the game called “E99” a game application of learning Asmaul Husna. This study involves 20 students aged 7 – 14 years old. The method used to carry out formative evaluation is the quantitative method and Perceived Usefulness and Ease of Use (PUEU) and Questionnaire for User Interface Satisfaction (QUIS) as an instrument. Overall findings show this application 89% useful, 83.75% ease of use and 85.5% satisfying. It indicates E99 is effective game for help students recognize and memorize Asmaul Husna.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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".