Determining paragraph type from paragraph position

Kyle B. Dempsey, Philip M. McCarthy, John C. Myers, Jennifer Weston, Danielle S. McNamara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Students must be able to competently compose essays in order to succeed in school and progress into the workplace. Current intelligent tutoring systems (ITS) attempt to provide individual training that is lacking in the current educational system. To provide efficient individual training through ITS, the systems must be able to effectively assess writing input from students. Necessary components for computer-based writing tutors are algorithms that mimic human judgments of writing. The current study attempts to establish a connection between paragraph position and human ratings of paragraph type through the use of computational measures provided by Coh-Metrix. We find that expert raters do not easily identify paragraph type and ratings of paragraph type do not map onto paragraph position.

Original languageEnglish (US)
Title of host publicationProceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22
Pages33-38
Number of pages6
StatePublished - Nov 4 2009
Externally publishedYes
Event22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22 - Sanibel Island, FL, United States
Duration: Mar 19 2009Mar 21 2009

Publication series

NameProceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22

Other

Other22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22
Country/TerritoryUnited States
CitySanibel Island, FL
Period3/19/093/21/09

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

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