School age children's cognition identification by mining integrated computer games data
Bibliographic record
Abstract
National statistics confirm that nowadays about 20% of school children have some type of mental health issue, about 70% of adult mental health disorders originated in adolescence, while about 40% have unidentified learning differences that affect their learning abilities. Starting early enough with proper screening of a child's cognitive skills is critical for improved learning and mental wellbeing. Unfortunately, assessments and treatment can be costly, elusive or conflicting. This paper describes an approach to identifying child's cognitive skill level that is adopted by an online product called "Think2Learn" developed by OTEP Inc. (Online Training & Education Portal). OTEP uses ubiquitousness of the Internet and attractive features of online computer games to give parents automated opportunity to screen and follow their children's cognitive development. OTEP presently uses a collection of approximately 100 video games for children to play and while doing so, it records their score to continuously assess and monitor their cognitive strengths and weaknesses. The Web-based tool for identifying cognitive skill level is developed as an integration or data warehouse of a number of relevant data sources such as the cognitive skills categories data, games data, player inventory data and so on. The integrated data are continuously mined, analyzed and queried for proper and quick assessment or recommendations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".