Serious Game for the Screening of Central Auditory Processing Disorder in School-Age Children: Development and Validation Study
Bibliographic record
Abstract
BACKGROUND: Currently, many central auditory processing disorder screening tests are available for children, and serious games (SGs) are frequently used as a tool for the diagnosis of different neural deficits and disorders in health care. However, it has not been possible to find a proposal that unifies both ideas. In addition, the validation and improvement of SGs, in general, does not take into account the player-game interaction, thus omitting valuable information about the playability and usability of the game. OBJECTIVE: This study presented Amalia's Planet, a game conceived for use in school environments, which allows a first assessment of a child through their performance of the proposed tasks related to different aspects of auditory performance. In addition, the game defines a series of events in relation to the execution of the tasks, which were evaluated for the subsequent optimization of its performance and the improvement of its usability. METHODS: Using screening tools based on the use of SG technologies, a total of 87 school-age children were evaluated to test the various hypotheses proposed in this study. By grouping users according to whether they had personal history of hearing pathologies, the discriminant power, playability, and usability of the final solution were examined using traditional statistical techniques and process mining (PM) algorithms. RESULTS: With a confidence level of 80% for test 2 (P=.19), there was no statistical evidence to reject the null hypothesis that a player's performance is affected by whether the player had a previous auditory pathology. Furthermore, the tool allowed the screening of 2 players initially categorized as healthy because of their low level of performance in the tests and the similarity of their behavior with that of the group of children with a previous pathology. With regard to the validation of the proposed solution, the use of PM techniques made it possible to detect the existence of events that lasted too long, which can lead to player frustration, and to discover small structural flaws in the game. CONCLUSIONS: SGs seem to be an appropriate tool for the screening of children at risk of central auditory processing disorder. Moreover, the set of PM techniques provides a reliable source of information regarding the playability and usability of the solution to the development team, allowing its continuous optimization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".