Technology-enhanced Programs for Children with Autism: implementing Applied Behavior Analysis Intervention on Mobile Devices

Autism Spectrum Disorder (ASD) is a pervasive developmental disorder characterized by difficulties in communication and social integration. Many research studies provide evidence that early and intensive intervention (preschool age 4-6 hours/day) leads to great progress in skills, and in some cases brings the child to a developmental level equal to his/her peers. Since traditional educational methods are rarely effective in ASD, new teaching approaches aimed at better exploiting the subject’s abilities are currently adopted. Among these, early intervention based on Applied Behavior Analysis (ABA) offers children with autism the possibility of learning in an accessible structured way, adapting pace, format and feedback to the subject’s abilities. Considering technology-enhanced learning, several studies have shown the effectiveness of computer-assisted programs for special education of autistic children due to the intrinsic ‘distance’ between the PC and the child that does not require emotional involvement (a computer does not require interpretation of emotion) allowing the repeatability of answers (reducing anxiety). Although there is a great availability of educational software programs, few of them apply ABA principles. Most of them are commercial products having additional costs for families. Moreover, there are still no formal protocols for testing the learning software for autistic subjects and further studies are necessary in the field of human-computer interaction when the interaction is performed by an individual with autism. In this thesis, I describe the design and implementation of a set of software tools that comprise ABCD SW (Autistic Behavior & Computer-based Didactic SoftWare), an ABA compliant application, designed to support children and tutors in a 1-to-1 learning process. ABCD SW is an open-source adaptive software based on Augmentative and Alternative Communication (AAC) and Discrete Trial Training (DTT), specifically developed for an early intervention (2-6 years old). The software allows children to perform ABA trials, automatically storing all data of the sessions. The tutor must integrate the evaluation data (prompt provided, behavior) by pressing just one key. A learning analytic tool extracts data, offering real-time monitoring of children’s learning for assessing at a glance progress or problems experienced by each child. The effectiveness of ABA intervention supported by ABCD SW has been tested via a pilot study with seven autistic children. The efficacy was evaluated through two data sources: 1) subjective feedback collected through an online survey proposed to the ABA team of the children involved in the user test; 2) an objective assessment based on learning analytics trends highlighted by the ABCD software correlated with the children’s evaluations. These evaluations were made by a psychologist before and after the user test, through Vineland adaptive behavior scales for measuring personal and social skills. Results show that children manifested improvement in communication, especially in the expressive communication sub-category (p < 0.05). Furthermore, the ABA team observed that children improved in communication, socialization and behavior. The pilot study suggests effectiveness of rehabilitation of autistic children using this ABA technology-enhanced intervention

Designing a mobile application to record ABA data

Applied Behavior Analysis (ABA) is a scientific method for modelling human behavior, successfully applied in the context of educating autistic subjects. ABA's scientific approach relies on recording measurable data derived from the execution of structured programs. In this paper we describe an application designed to support the work of ABA tutors with autistic subjects. Specifically, we describe an Android application for gathering data from ABA sessions with a patient and sharing information among his/her ABA team. Tablets allow mobility and ease of interaction, enabling efficient data collection and processing, and automating tasks previously carried out by recording notes on paper. However, reduced screen size poses challenges for user interface design