Automated approaches for detecting integration in student essays

Simon Hughes, Peter Hastings, Joseph Magliano, Susan Goldman, Kimberly Lawless

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

5 Scopus citations


Integrating information across multiple sources is an important literacy skill, yet there has been little research into automated methods for measuring integration in written text. This study investigated the efficacy of three different algorithms at classifying student essays according to an expert model of the essay topic which categorized statements by argument function, including claims and integration. A novel classification algorithm is presented which uses multi-word regular expressions. Its performance is compared to that of Latent Semantic Analysis and several variants of the Support Vector Machine algorithm at the same classification task. One variant of the SVM approach worked best overall, but another proved more successful at detecting integration within and across texts. This research has important implications for systems that can gauge the level of integration in written essays.

Original languageEnglish (US)
Title of host publicationIntelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings
Number of pages6
StatePublished - 2012
Event11th International Conference on Intelligent Tutoring Systems, ITS 2012 - Chania, Crete, Greece
Duration: Jun 14 2012Jun 18 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7315 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Intelligent Tutoring Systems, ITS 2012
CityChania, Crete

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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