An Evaluation of The Mobile Apps for Children with Special Education Needs Based on The Utility Function Metrics

Mobile apps can be used in various environments and at any time. People used them for learning, communications, and entertainment. Because of the ease use of mobile devices interface (like smartphone and tablet), then everyone, including the children with special needs, can have used them. In recent years, there has been an increase in the efforts of educational institutions and of scientists to support children in their daily life. Ongoing developments in communication and information technologies contribute to this process. The main goal of this study is to present the basic functional requirements for the mobile apps for children with special needs. The current state of the scientific research related to the design and development of mobile apps is discussed. This issue became very important in the last years because of an increase in the number of children with special needs on a worldwide scale is observed. And the same time the increase in the use of mobile technologies of them. The proposed model for the evaluation of potential utility provides for the classification of the mobile applications designed for children with special needs about their functionality features. This model is based on our studies of the state-of-art scientific works of many authors. Whit the model for the evaluation of potential utility, the 27 mobile applications for children with special needs, downloaded from the mobile application stores: Apple Store, Google Play and Store Windows Phone Apps, were classified and analyzed. The results showed that despite the variety of mobile applications, those that are suitable for children with special needs are too few. Most of the applications cover only half of the evaluation criteria, which means they have functionalities only for individual needs. Therefore, the proposed utility function metrics of the evaluation can be used as a basis for interface developing for mobile apps, appropriate for children with special needs

Automated extraction of speech and turn-taking parameters in autism allows for diagnostic classification using a multivariable prediction model

Autism spectrum disorder (ASD) is diagnosed on the basis of speech and communication differences, amongst other symptoms. Since conversations are essential for building connections with others, it is important to understand the exact nature of differences between autistic and non-autistic verbal behaviour and evaluate the potential of these differences for diagnostics. In this study, we recorded dyadic conversations and used automated extraction of speech and interactional turn-taking features of 54 non-autistic and 26 autistic participants. The extracted speech and turn-taking parameters showed high potential as a diagnostic marker. A linear support vector machine was able to predict the dyad type with 76.2% balanced accuracy (sensitivity: 73.8%, specificity: 78.6%), suggesting that digitally assisted diagnostics could significantly enhance the current clinical diagnostic process due to their objectivity and scalability. In group comparisons on the individual and dyadic level, we found that autistic interaction partners talked slower and in a more monotonous manner than non-autistic interaction partners and that mixed dyads consisting of an autistic and a non-autistic participant had increased periods of silence, and the intensity, i.e. loudness, of their speech was more synchronous