| Summary: | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. === This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. === Cataloged from student-submitted PDF version of thesis. === Includes bibliographical references (pages 63-64). === MIT App Inventor is a visual blocks language that allows users to create mobile applications for Android. App Inventor users have the option of posting in a public support forum to discuss anything from specific programming issues to education issues. In order to leverage the information on the forums to improve App Inventor, we must first understand what is being discussed. In this thesis, we used unsupervised machine learning methods to automate discovery of discussion topics. First, we transformed posts into feature vectors using a bag-of-words model. Next, we clustered posts using k-means clustering and evaluated our results both quantitatively, by calculating the average silhouette of the posts, and qualitatively, by simply looking at the clusters of posts. Finally, we used LDA topic modeling to determine the topics being discussed and compared the extracted topic words to a manual evaluation of each cluster. Using this technique, we were able to uncover common problems with App Inventor that users encountered. We hope to use this information to improve users' experience with App Inventor. === by Sylvan Tsai. === M. Eng.
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