A behavioural framework for designing educational computer games

Research has indicated that computer games can be innovative and powerful tools for education. Indeed, combining psychological research and games design principles offers a framework for developing educational games that promote learning while maintaining high motivation of the players. If designed correctly, it appears that games can utilize the inherent motivation demonstrated by game players to teach skills that are of immediate practical benefit. The current paper explores “the edges of gaming” in terms of proposing a novel theoretical and methodological framework for the design of educational games

Brain–computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum

Individuals with Autism Spectrum Disorder (ASD) show deficits in social and communicative skills, including imitation, empathy, and shared attention, as well as restricted interests and repetitive patterns of behaviors. Evidence for and against the idea that dysfunctions in the mirror neuron system are involved in imitation and could be one underlying cause for ASD is discussed in this review. Neurofeedback interventions have reduced symptoms in children with ASD by self-regulation of brain rhythms. However, cortical deficiencies are not the only cause of these symptoms. Peripheral physiological activity, such as the heart rate, is closely linked to neurophysiological signals and associated with social engagement. Therefore, a combined approach targeting the interplay between brain, body and behavior could be more effective. Brain-computer interface applications for combined neurofeedback and biofeedback treatment for children with ASD are currently nonexistent. To facilitate their use, we have designed an innovative game that includes social interactions and provides neural- and body-based feedback that corresponds directly to the underlying significance of the trained signals as well as to the behavior that is reinforced

Autism and the U.K. secondary school experience

This research investigated the self-reported mainstream school experiences of those diagnosed on the autistic spectrum compared with the typically developing school population. Existing literature identifies four key areas that affect the quality of the school experience for students with autism: social skills, perceived relationships with teaching staff, general school functioning, and interpersonal strengths of the young person. These areas were explored in a mainstream U.K. secondary school with 14 students with autism and 14 age and gender matched students without autism, using self-report questionnaires and semi-structured interviews. Quantitative analyses showed consistent school experiences for both groups, although content analysis of interview data highlighted some differences in the ways in which the groups perceive group work, peers, and teaching staff within school. Implications for school inclusion are discussed, drawing attention to how staff awareness of autism could improve school experience and success for students with autism attending mainstream schools

The use of humor by an adolescent with autism spectrum disorder

The aim of this study was to describe the humor use by an 11-year-old adolescent with ASD. Through an iterative coding process to identify a successful, unsuccessful, and neutral rating of instances of humor, we describe how does an adolescent with ASD uses humor and the functions humor serves in his interactions with a friend. We describe the personal and environmental factors that support the successful use of humor. The adolescent used two main types of humor (self-initiated and environmentally-initiated), consisting of two forms (verbal and physical). We describe 12 main behavioral indicators to identify the instances of humor. Humor appeared to sever as a means of engaging his friend and he appeared to be most successful in using humor with his friend when in a familiar environment and engaging in a familiar activity. The implications for future research are discussed

A Survey: Approaches for Detecting the Autism Spectrum Disorder

A brain disease mean autism spectrum disorder affects a person's ability to connect, communicate, and remember. Though autism is capable of being diagnosed regardless of age, most of the disorder's signs begin to appear around its initial two years of life and increase as time goes on. People with autism suffer from a wide range of difficulties, such sensory problems, action impairments, intellectual disabilities, and psychological disorders including depression and anxiety. Autism has been rising at an unacceptably rapid pace surrounding around the globe. Autism detection involves an enormous amount of time and money. The early detection of autism might be highly advantageous in regards to treating patients with the right medical treatments at the correct moment in time. It could prevent the individual's illnesses before developing severe and could help in decreasing future expenses associated to a diagnosis that was delayed. Thereby, the requirement to develop a rapid, trustworthy, and simple examination device that can make predictions is essential. Autism Spectrum Disorder (ASD) has been gaining momentum presently more quickly than at any time earlier. Diagnostic evaluation of autistic characteristics is extremely expensive and time-consuming as well. The advancement of algorithms for machine learning (ML) and Artificial intelligence (AI) have made it achievable to identify autism fairly earlier. Although the reality of numerous studies have been carried out performed utilising different techniques, these studies have not contributed to any definitive conclusions regarding the capacity of predicting autism attributes in regards to different age categories. Thereby, the objective of this research is to predict Autism among people of all ages and to provide an effective model for prediction using various ML approaches