2026-09-18T21:59:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1298622026-06-06T00:49:28Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Andrew Sliwinski.
Abdalla, Lena(Lena A.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-02-19T20:26:08Z
2021-02-19T20:26:08Z
2020
2020
https://hdl.handle.net/1721.1/129862
1237279491
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 133-136).
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. Overall, I demonstrate that NLP techniques can be used in the classification of computer programs to a reasonable level of accuracy.
by Lena Abdalla.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
136 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Classification of computer programs in the Scratch online community
Thesis
application/pdf
2021-02-19T20:25:38Z
Publication
Master
EECS
unknown
unknown
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<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. Overall, I demonstrate that NLP techniques can be used in the classification of computer programs to a reasonable level of accuracy.</Abstract>
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