Multimodal Acquisition and Analysis of Children Handwriting for the Study of the Efficiency of Their Handwriting Movements: The @MaGma Challenge
Bibliographic record
Abstract
Handwriting is a fundamental skill that each pupil should master for successfully completing his instructions. However, for a certain number of children, after a successful phase of handwriting learning an unexplained rough deterioration of their efficiency on paper is observed during the phase of customization of their handwriting. In this context, our goal is to make a comprehensive study of the evolution and the difficulties of children handwriting learning according to handwriting teaching approaches involved in school. To achieve such a challenge, it is necessary to collect in a secured way and to analyze a large amount of various contextualized online and offline handwritten data produced in real scholar situations by numerous pupils from kindergarten up to middle school. This is the purpose of the ongoing @MaGma project that was defined with the Academic direction of the Guadeloupian Region. In this paper, we specify the problems handled in @Magma and depict the general principles which will have to govern the collaborative infrastructure of acquisition and treatment of children's writing, based on 2 frameworks: Copilotr@ce and Dekattras. Next, we report the preliminary results of the comparative sigma-lognormal and dynamic analysis of a set of children scribbles acquired thanks to this infrastructure. We conclude by developing how these first results obtained in a real scholar acquisition context confirm the experimental results previously obtained in more clinical contexts, pointing out the fact that the Personalized Digital Bodyguard concept and vision is realizable.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".