Design an Applied-Behavior-Analysis Learning WeChat Tool to Assess the Learning Capacities for Autistic Children

Abstract

Autism is a developmental disorder caused by neurological diseases. The applied behavior analysis (ABA) has currently been considered as one of the most researchsupported and effective autism interventions. In this paper, we design a Wechat learning tool to assist the learning of the applied behavior analysis class for autistic children. This tool utilizes the WeChat public account as the convenient entrance with the front-end and back-end development modes separated. Vue and Spring Boot are used as front-end and back-end frameworks, MySQL and the Hibernate framework are used for data management. The learning activities of ABA classes can be effectively planned and tracked, and these activities can be analyzed for improvement without interfering the conventional ABA learning process. We conduct a performance evaluation study to assess the capacity of the system and the accuracy of scoring ABA activities using machine learning algorithms based on simulated data. This system may provide the measurement platform to track the multi-modal learning activities by integrating other intelligent terminals to build a comprehensive information-assisted autism class for personalized learning.