@article {10.3844/jcssp.2023.1124.1131,
article_type = {journal},
title = {A Learning Mechanism to Improve Skills Acquisition for Autistic Children in Mauritania},
author = {Seyidi, Cheikhne Mohamed Mahmoud and Seyed, Cheikhane and El Arby, Mohamed El Moustapha and Diakité, Mohamed Lamine and NANNE, Mohamedade Farouk},
volume = {19},
number = {9},
year = {2023},
month = {Aug},
pages = {1124-1131},
doi = {10.3844/jcssp.2023.1124.1131},
url = {https://thescipub.com/abstract/jcssp.2023.1124.1131},
abstract = {The learning of autistic children is an issue for care and treatment centers for children with autism spectrum disorder, however many studies seek to find learning mechanisms and models in order to facilitate their social integration. The aim of this study is to propose a learning mechanism based on the analysis and annotation of a corpus video filmed during the learning and training sessions of autistic children in Mauritania. We have analyzed these videos with the objective of identifying a classical and software annotation and then producing a coding schema while importing it into ANVIL software. In this respect, we are interested in the XML files generated by ANVIL presenting details of the appearance of attributes in the annotated videos. The match between the two annotations illustrates that the data in the files converge in showing that the children who had an improvement in skill acquisition in the treatment sessions due to the fact that the specialist educators in the videos emphasized attributes that promote collaboration more than others to get the children's attention. Indeed, we wish to obtain a learning mechanism that is as complete as possible for the schooling of autistic children in Mauritania and that can later be implemented as a pedagogical conversational agent.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}