{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/129862"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/129862","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Classification of computer programs in the Scratch online community","abstract":"Scratch is a graphical programming platform that empowers children to create computer programs and realize their ideas. Although the Scratch online community is filled with a variety of diverse projects, many of these projects also share similarities. For example, they tend to fall into certain categories, including games, animations, stories, and more. Throughout this thesis, I describe the application of Natural Language Processing (NLP) techniques to vectorize and classify Scratch projects by type. This effort included constructing a labeled dataset of 873 Scratch projects and their corresponding types, to be used for training a supervised classifier model. This dataset was constructed through a collective process of consensus-based annotation by experts. To realize the goal of classifying Scratch projects by type, I first train an unsupervised model of meaningful vector representations for Scratch blocks based on the composition of 500,000 projects. Using the unsupervised model as a basis for representing Scratch blocks, I then train a supervised classifier model that categorizes Scratch projects by type into one of: \"animation\", \"game\", and \"other\". After an extensive hyperparameter tuning process, I am able to train a classifier model with an F1 Score of 0.737. I include in this paper an in-depth analysis of the unsupervised and supervised models, and explore the different elements that were learned during training. 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