Attention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, constructing such systems is a challenging task due to the complexity of attentional behavior in ASD. This paper proposes a face-based attention recognition model using two methods. The first is based on geometric feature transformation using a support vector machine (SVM) classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a convolutional neural network (CNN) approach. We conducted an experimental study on different attentional tasks for 46 children (ASD n=20, typically developing children n=26) and explored the limits of the face-based attention recognition model for participant and task differences. Our results show that the geometric feature transformation using an SVM classifier outperforms the CNN approach. Also, attention detection is more generalizable within typically developing children than within ASD groups and within low-attention tasks than within high-attention tasks. This paper highlights the basis for future face-based attentional recognition for real-time learning and clinical attention interventions.

It has long been reported that children with autism spectrum disorder (ASD) exhibit attention difficulties while learning. They tend to focus on irrelevant information and can easily be distracted. As a result, they are often confined to a one-to-one teaching environment, with fewer distractions and social interactions than would be present in a mainstream educational setting. In recent years, inclusive mainstream schools have been growing in popularity due to government policies on equality rights. Therefore, it is crucial to investigate attentional patterns of children with ASD in mainstream schools. This study aims to explore the attentional behaviors of children with ASD in a virtual reality simulated classroom. We analyzed four eye-gaze behaviors and performance scores of 45 children: children with ASD (ASD n = 20) and typically developing children (TD n = 25) when performing attention tasks. The gaze behaviors included time to first fixate (TTFF), first fixation duration (FFD), average fixation duration (AFD) and the sum of fixation count (SFC) on fourteen areas of interest (AOIs) in the classroom. Our results showed that children with ASD exhibit similar gaze behaviors to TD children, but with significantly lower performance scores and SFC on the target AOI. These findings showed that classroom settings can influence attentional patterns and the academic performance of children with ASD. Further studies are needed on different modalities for supporting the attention of children with ASD in a mainstream setting.

Attention is one of the fundamental elements of effective learning. The design of learning environments often consists of a blend of visual stimuli. Investigating the effect of visual stimuli types on the attention of children with autism spectrum disorder (ASD) is important for the theoretical understanding of attention. This study explores the effect of social and nonsocial visual stimuli on the attention of children with ASD and typically developing (TD) children in a simulated virtual classroom. Forty-six participants (ASD = 20, TD = 26) took part in a series of attention tests, in which social and nonsocial visual stimuli were used as target stimuli. We examined four eye-gaze measures: time to first fixate, first fixation duration, average fixation duration, and the sum of fixation count. The results show that social and nonsocial stimuli do not affect the attention of ASD and TD children. However, TD children pay significantly greater attention to target stimuli than children with ASD. These findings emphasize the strengths of children with ASD during attention tasks and the potential for the use of eye-gaze measures to identify attention impairment in children with ASD. This study thus recommends an investigation methodology for on-task attention assessment in a learning environment.