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 sigmalognormal and dynamic analysis of a set of children scribbles acquired thanks to this infrastructure.We conclude by deve oping how these first results obtained in a real scholar acquisition context confirm the xperimental results previously obtained in more clinical contexts, pointing out the fact that the Personalized Digital Bodyguard concept and vision is realizable.