skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Award ID contains: 2300645

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; (, American Association of Physics Teachers)
    We analyzed the essays that were written on various topics in an introductory physics course using two unsupervised machine learning algorithms. One of them was Latent Dirichlet Allocation (LDA). This algorithm is used for extracting abstract topics from a collection of text documents. The other algorithm was Non-negative Matrix Factorization (NMF). It is used for similar purposes but also in other domains such as image recognition. We applied these two algorithms to the dataset that consisted of N=683 student essays. Although there were some built-in, important differences between LDA and NMF, they both found similar topics in our data by large. This offers instructors a promising and productive way of accessing useful information about their students' written work, especially in large-enrollment classes. 
    more » « less
    Full Text Available