Reading Rocks Junior: A Mixed-Methodological Study of the Efficacy and Accessibility of an Emergent Literacy Intervention

This thesis explored early literacy development in young vulnerable readers. More specifically, this thesis examined an emergent literacy program called Reading Rocks Junior offered by the Learning Disabilities Association of Niagara Region to children four- to six-years of age living in low socioeconomic status communities. Three methodologies were combined to create a rich and complete picture of an effective and accessible literacy program. First of all, a description of the Reading Rocks Junior program is outlined. Secondly, quantitative data that was collected pre- and post- program was analyzed to demonstrate achievement gains made as a result of participating in the program. Finally, qualitative interviews with the program coordinator, the convener of the agency that funded Reading Rocks Junior and three parents whose children participated in the program were analyzed to determine the contextual factors that make Reading Rocks Junior a success.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).