Penerapan Dpe Framework Dalam Perancangan Serious Game Bagi Pengidap Kebutaan Nada
Bibliographic record
Abstract
Tone deafness, wich known as congesti amusia, is kind of disability to sing the note correctly. Tone deafness can be detected through tests that apply the montreal battery method, used to determine a person's ability to compare the level of tone and recognize patterns of ups and downs. Based on the problems above, then designed serious game that aims to therapy for people with tone deafness. The design of this serious game is implementing DPE (Design, Play, Experience) framework, that divides designing process into multiple layers. These layers include: learning layer, layer storytelling, gameplay layer, user experience layer, and the technology layer. The tests wich conducted in this serious game is play test and tone deafness competency test. In play test, questionnaires will be used as a testing tool, containing questions related to the player's interest towards content and idea of this game. On the tone deafness competency test, participants ability to descriminate tones will be analysed, which is represented by the score of the tone deafness test. From the results of these tests, can be concluded that serious games are designed in compliance with the objectives learning as well as the excitement factor wich is the essence of a game.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".