This study describes the language environment of preschool programs serving children with autism spectrum disorders (ASDs) and examines relationships between child characteristics and an automated measure of adult and child language in the classroom. The Language Environment Analysis (LENA) system was used with 40 children with ASD to collect data on adult and child language. Standardized assessments were administered to obtain language, cognitive, and autism severity scores for participants. With a mean of over 5 hours of recording across two days several months apart, there was a mean of 3.6 child vocalizations per minute, 1.0 conversational turns (in which either the adult or child respond to the other within 5 seconds) per minute, and 29.2 adult words per minute. Two of the three LENA variables were significantly correlated with language age-equivalents. Cognitive age-equivalents were also significantly correlated with two LENA variables. Autism Diagnostic Observation Schedule severity scores and LENA variables were not significantly correlated. Implications for using the LENA system with children with ASD in the school environment are discussed.

No abstract

The paraeducator workforce as well as the breadth of their responsibilities to serve students in special education has increased considerably in public schools. Unfortunately, research to identify the most effective methods for training paraeducators has not kept pace. Addressing this dynamic, through an implementation science framework, requires a better understanding of contextual factors. In this study, focus groups (FGs) with paraeducators and teachers were conducted to examine (a) responsibilities, training needs, and related issues of paraprofessionals; (b) training needs and related issues of teachers as supervisors of paraeducators; and (c) factors that support or impede special educators’ self-efficacy and their perception of competence of their colleagues. Four FGs, homogeneous based on role and assignment (mild vs. moderate-to-severe disabilities), were conducted utilizing a semistructured interview protocol. Qualitative content analysis revealed important challenges and training-related deficiencies experienced by both teachers and paraeducators, as well as suggestions for addressing these issues.

No abstract

Early educators in their professional practice are expected to use data-based decision making when deciding which children may be at risk, and make changes in interventions based on data (Akers et al., 2015a, 2015b; Division for Early Childhood, 2014). While these concepts and practices are promoted in early intervention and early childhood special education (Division for Early Childhood, 2014), only recently have measures designed for use by practitioners engaged in universal screening and progress monitoring emerged in early childhood (Kaminski, Abbott, Bravo-Aguayo, Latimer, & Good, 2014; McConnell, Wackerle-Hollman, Roloff, & Rodriguez, 2014). These measures are very good at identifying children who are and are not making progress in the curriculum. However, because they are focused on learning outcomes, little information is available about what the instructional problem conditions are, the most likely treatment adaptations needed, and how such decisions should vary given a child with a weak literacy skill level (literacy risk) and/or an Individualized Education Plan (IEP). The implications of this information gap crosscuts nationally and locally reported trends in (a) low levels of instructional supports provided to children by preschool teachers in early education (Justice, Hamre, & Pianta, 2008) and inclusive early childhood special education classrooms (Carta & Driscoll, 2013; Guo, Sawyer, Justice, & Kaderavek, 2013), and (b) the high rate of expulsion of children struggling to adhere to classroom routines (Gillam et al., 2008). Children who are not responding to intervention are not learning from the experiences arranged for them by the teacher. Several approaches exist that seek to identify instructional problem conditions surrounding a child's lack of response, and suggest change in instructional intervention, including functional behavioral assessment (FBA) and ecobehavioral assessment (EBA; Watson, Gable, & Greenwood, 2010). The goal of both approaches is to investigate response contingencies (environment-behavior interactions) that are 741968T ECXXX10.