Scalable methods for navigating large annotation collections in NB
Name
Schoen-amschoen-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
Size
1.5 MB
Format
Adobe PDF
Checksum (MD5)
dd79fdf949d2964f2eeae2f032a6c2d7
Author(s)
Schoen, Alizee
Advisor(s)
Karger, David
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
NB is an online tool where students can annotate readings and lecture notes, while also discussing with other classmates and instructors. Currently, classes that are using NB have hundreds of students, which results in thousands of annotations per document. After discussing with users of NB, and looking at other platforms, we found methods for students to navigate through the large collections of annotations. These methods include having statistics for each document, the ability to endorse a comment, follow authors, and minimize the number of comments on a document. Once these features were implemented, we studied their impact on NB by collecting user engagement data and feedback.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
