Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.

In co-taught classes, general education and special education teachers can improve the content-area learning and literacy skills of students with learning disabilities by helping them read texts effectively. Co-teachers can improve comprehension by providing students with background and vocabulary knowledge before reading. In this article, a routine for introducing background (world) and vocabulary (word) knowledge—the world knowledge and word knowledge routine (world and word)—is described. The article includes explanations how each part of the routine works and uses an example to illustrate how co-teachers could use the routine to promote student reading comprehension.

Several strategies that demonstrate promise are available for educators to improve reading comprehension outcomes for students. However, some students, including students with and at risk for learning disabilities, require more intensive supports to develop proficiency in reading comprehension. To support these students, teachers must intensify instruction. This article describes an intensive main idea identification strategy, sentence-level gist, for teachers to use with students with persistent reading comprehension difficulties in the co-taught classroom. The sentence-level gist strategy requires students to determine the subject and important words in each sentence and then synthesize this information to write a main idea statement for a section of a text.

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