MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Classification of computer programs in the Scratch online community

Author(s)
Abdalla, Lena(Lena A.)
Thumbnail
Download1237279491-MIT.pdf (8.799Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Andrew Sliwinski.
Terms of use
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
Metadata
Show full item record
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.
Description
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).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/129862
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.