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Frazan Soufizadeh Thesis Defense
Frazan Soufizadeh will give a graduate thesis defense of “Learning Material Classification” for the School of Information Technology.
In this study, we will investigate the applicability of text mining for categorizing different text-based educational resources into desired learning objectives by utilizing a TF-IDF feature selection algorithm combined with a majority-voting-based classification system comprised of five different classical classifiers. Three different knowledge domains with 65 learning objectives in total will be used in the experiments to evaluate the performance of the system. We will also propose a hierarchical multi-tier classification architecture and show that it can outperform single-layer single-node classification system in terms of computational cost and scalability.