Assessment of LDAT as a grammatical diversity assessment tool

Scott L. Healy, Joseph D. Weintraub, Philip M. McCarthy, Charles E. Hall, Danielle S. McNamara

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

3 Scopus citations

Abstract

The purpose of this study is to evaluate the validity of measuring grammatical diversity with a specifically designed Lexical Diversity Assessment Tool (LDAT). A secondary objective is to use LDAT to determine if the level of difficulty assigned to English as a Second Language (ESL) texts corresponds to increases in grammatical, lexical, and temporal diversity. Other methods of lexical diversity assessment, such as type-token ratio (TTR), have been used with varying accuracy in an effort to determine the complexity or level of texts. We analyzed 120 ESL texts independently assigned by their sources to one of four levels (Beginner, Lower-intermediate, Upper-intermediate, and Advanced). We demonstrated that LDAT significantly reflected the grammatical diversity within these texts. While the findings conflicted with the prediction that grammatical and lexical diversity would increase with assigned level, we concluded that the implementation of LDAT in text design could provide reliable assessments of grammatical diversity.

Original languageEnglish (US)
Title of host publicationProceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22
Pages249-253
Number of pages5
StatePublished - 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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