Blended Learning Through an Interactive Mobile Application for Teaching Autistic Kindergarten Students
Bibliographic record
Abstract
The mobile applications industry has had significant growth in the last few years. Mobile phones are everywhere since we use them in every part of our daily lives for entertainment, communication and other various uses. Unfortunately, there was also a substantial increase the number of autism cases in kids around the world, which has prompted for a dire need of a therapy method that is cheap, reliable and accessible for everyone who needs it. Researchers have tried several methods, like robotics and virtual reality, to help in the therapy of autistic children. While their results were promising, these technologies are still out of reach of most users due to their high cost. Mobile phones, however, are much more accessible since everyone has one, and they have a wide array of useful gadgets that can be used in making the therapy sessions more engaging and fun such as cameras, accelerometers, speakers, microphones and others. This project aims to design and implement an interactive learning environment based on a mobile application for teaching kids with special needs.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".