The Impact of Professional Development Training in Autism and Experience on Teachers\u27 Self-Efficacy

Regular education teachers\u27 self-efficacy may be negatively impacted due to a lack of professional development and experience teaching students with Autism Spectrum Disorder (ASD). Research links teacher self-efficacy with increased student academic achievement. The purpose of this study was to examine to what degree training on ASD during and following teacher certification and experience had on overall teacher self-efficacy. This one-shot case study was based upon Bandura\u27s theoretical construct of self-efficacy and secondarily on Tschannen-Moran, Woolfolk Hoy, and Hoy\u27s theory of self-efficacy. The Teachers\u27 Sense of Efficacy Scales (TSES) was used to collect data from regular education teachers with experience teaching students with ASD in 1st through 3rd grades in a Southern California school district. After the data were assessed for accuracy, missing data, and outliers, the analysis was conducted on 36 cases. MANOVAs were conducted to assess differences on overall self-efficacy. Separate ANOVAs were used since the overall self-efficacy and the subscores were highly correlated. Though the sample in this study was small (n = 36) for data analysis, the effect size showed that training experience and grade levels had a moderate to large effect on teacher self-efficacy (.16, .13, .13 respectively). Therefore teacher self-efficacy has a positive impact on student achievement. Implications for positive social change are self-efficacious teachers increase the academic achievement of students with ASD. In this way, such students can become self-sustaining, dynamic members of the work force and community

Estensione dell\u27algoritmo di estrazione dei grafi di Reeb a superfici tridimensionali

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Data-driven quasi-interpolant spline surfaces for point cloud approximation

In this paper we investigate a local surface approximation, the Weighted Quasi Interpolant Spline Approximation (wQISA), specifically designed for large and noisy point clouds. We briefly describe the properties of the wQISA representation and introduce a novel data-driven implementation, which combines prediction capability and complexity efficiency. We provide an extended comparative analysis with other continuous approximations on real data, including different types of surfaces and levels of noise, such as 3D models, terrain data and digital environmental data

Weighted Quasi Interpolant Spline Approximations: Properties and Applications

Continuous representations are fundamental for modeling sampled data and performing computations and numerical simulations directly on the model or its elements. To effectively and efficiently address the approximation of point clouds we propose the Weighted Quasi Interpolant Spline Approximation method (wQISA). We provide global and local bounds of the method and discuss how it still preserves the shape properties of the classical quasi-interpolation scheme. This approach is particularly useful when the data noise can be represented as a probabilistic distribution: from the point of view of nonparametric regression, the wQISA estimator is robust to random perturbations, such as noise and outliers. Finally, we show the effectiveness of the method with several numerical simulations on real data, including curve fitting on images, surface approximation and simulation of rainfall precipitations