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While the knowledge that has been gained from previous studies has accelerated the understanding of the difficulties facing individuals with Autism Spectrum Disorders (ASDs), there is concern regarding the speed with which and the overall lack of translation of research into interventions that make differences in the everyday lives of individuals with ASDs (Gresham, et al., 2001; Volkmar, et al., 2004; Volkmar, Reichow, & Doehring, 2011). For example, the symptoms of ASDs can greatly impair an individual’s ability to navigate independently through everyday events. Translating this knowledge into instructional practice requires, then, the design of methods for easing students’ transitions within the school, home, and community. While research has validated the use of low-tech visual supports (e.g., National Autism Center, 2009), little has been done to analyze the utility and appropriateness of high-tech assistive technology, such as those interventions administered through smartphones, tablets, and other handheld devices, which are devices that are being used more frequently in education settings (Gray et al., 2010). This chapter presents the results of federally funded research to determine whether the use of iPrompts—a software application for iOS and Android-based smartphones and tablet computers—assists teachers and other educational professionals as they help students with ASD transition from one activity to the next or from one setting to another.
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Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but also on the level of the individual student. In this paper we propose an ensemble support vector machines based model for predicting students' graduation. Up to about 100 features, including a set of psychological-educational factors, were employed to construct the predicting model. We evaluated the proposed model using data taken from a state university's longitudinal, cohort data sets from the incoming classes of students from 2011-2012 (n=350). The experimental results demonstrated the effectiveness of the model, with considerable accuracy, precision, and recall. This paper presents the results of analysis that were conducted in order to gauge the predictive capability of a machine learning algorithm to predict on-time graduation that took into consideration students' learning and development.
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Conventional methods of addressing the needs of students with print disabilities include text-to-speech services. One major drawback of text-to-speech technologies is that computerized speech simply articulates the same words in a text whereas human voice can convey emotions such as excitement, sadness, fear, or joy. Audiobooks have human narration, but are designed for entertainment and not for teaching word identification, fluency, vocabulary, and comprehension to students. This chapter focuses on the 3-year pilot of CRISKids; all CRIS recordings feature human narration. The pilot demonstrated that students who feel competent in their reading and class work tend to be more engaged in classroom routines, spend more time on task and demonstrate greater comprehension of written materials. When more demonstrate these behaviors and skills, teachers are better able to provide meaningful instruction, since less time is spent on issues of classroom management and redirection. Thus, CRISKids impacts not only the students with print disabilities, but all of the students in the classroom.
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