skip to main content

Explore Research Products in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Hulse, T."

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. ; ; ; ; ( , Proceedings of the Eleventh International Conference on Educational Data Mining)
    A substantial amount of research has been conducted by the educational data mining community to track and model learning. Previous work in modeling student knowledge has focused on predicting student performance at the problem level. While informative, problem-to-problem predictions leave little time for interventions within the system and relatively no time for human interventions. As such, modeling student performance at higher levels, such as by assignment, may provide a better opportunity to develop and apply learning interventions preemptively to remedy gaps in student knowledge. We aim to identify assignment-level features that predict whether or a not a student will finish their next homework assignment once started. We employ logistic regression models to test which features best predict whether a student will be a “starter” or a “finisher” on the next assignment. 
    more » « less
    Accepted Manuscript Full Text Available