Social language opportunities for preschoolers with autism: Insights from audio recordings in urban classrooms

Abstract

Many children diagnosed with autism spectrum disorder who receive early intervention reap developmental benefits, but little is known about characteristics of early intervention placements in the community that optimize individual growth. The extent to which children hear and use language, in particular, may contribute significantly to developmental outcomes. We analyzed natural language production and exposure to language in preschoolers on the autism spectrum across three classroom compositions: autism only, mixed disability, and inclusion. Autistic children in inclusion classrooms produced more speech, received significantly more verbal input from their peers, and were exposed to a similar amount of teacher talk compared to children in autism only or mixed disability classrooms. These findings shed preliminary light on the linguistic environment of early intervention placements in the community, along with the characteristics of children placed in early intervention settings that may influence their language exposure from peers and teachers. Natural language sampling is a promising method for capturing language exposure in early intervention settings and providing context for understanding developmental outcomes resulting from early intervention. Lay abstract Early intervention is important for preschoolers on the autism spectrum, but little is known about early intervention classrooms in the community. This study found that children with better language skills and lower autism severity have more verbal interactions with their classmates, especially in classrooms with typically developing peers (inclusion settings). Findings suggest that natural language sampling is a useful method for characterizing autistic children and their early intervention settings. In addition, natural language sampling may have important implications for understanding individual opportunities for development in community early intervention settings.

Impact of Interactive Learning Environments on Learning and Cognitive Development of Children With Special Educational Needs: A Literature Review

“…When these language resources are shared, there is a considerable language growth effect on students in this sample. The work developed by Ferguson et al ( 2020 ), which focused on preschool students diagnosed with autism, points in the same direction. According to the results of their study, these students received greater verbal input, produced greater verbal output and had access to similar levels of teacher talk when they were integrated in inclusive classrooms compared with those who were in classes with only autist peers or in classes with peers with diverse disabilities.…”

“…Although further research is needed on this aspect, the 17 selected studies shed light on the importance of implementing interaction-based learning environments. Their benefits have been evidenced for developing language, literacy, and communication skills for SEN pupils (Whalon and Hart, 2011 , among others; Chen et al, 2020 ; Ferguson et al, 2020 ), for the acquisition of mathematical competence and science learning (Lambert et al, 2020 ; Lei et al, 2020 ; Wu et al, 2020 ) and for enhancing engagement in learning (Bock, 2007 ; Carter et al, 2017 ).…”

Reliably quantifying the severity of social symptoms in children with autism using ASDSpeech

“…A variety of previous studies have reported that individuals with ASD, on average, speak differently than TD individuals 4,[8][9][10][11][12][13][14][15][16] . According to these studies, ASD individuals exhibit atypical speech characteristics, including significantly fewer phonemes per utterance 11 , fewer conversational turns 13 , higher pitch 9,19 , and larger pitch range and variability 8,9 than TD children.…”

“…Recent studies have utilized speech analysis techniques to identify speech characteristics that differ in ASD children. These studies have reported that ASD children exhibit significantly fewer phoneme vocalizations [11], fewer vocalizations per minute [12], [13], fewer conversational turns (i.e., reciprocating in a conversation) [11], [13], [14], more non-speech vocalizations [12], [14], more distressed vocalizations (crying, screaming) [15], and a lower ratio of syllables to vocalizations [16] than TD children. Additional studies have trained machine learning and deep learning algorithms to classify ASD and TD children based on extracted speech features [17]–[24] and have reported classification accuracies of 75% to 98% [19].…”

“…Studies have reported, for example, that verbal ASD children tend to speak with higher pitch and larger pitch variability than typically developing (TD) children 8,9 . ASD children also exhibit significantly fewer phoneme vocalizations 11 , fewer conversational turns (i.e., reciprocating in a conversation) [11][12][13] , more non-speech vocalizations 12,14 , more distressed vocalizations (crying, screaming) 15 , and a lower ratio of syllables to vocalizations 16 than TD children.…”