Automated Developmental Sentence Scoring Using Computerized Profiling Software This study examined the accuracy of fully automated Developmental Sentence Scoring (DSS; L. L. Lee, 1974) analysis performed by the Computerized Profiling (CP) software (S. H. Long, M. E. Fey, & R. W. Channell, 2000). Samples from 48 school-age children (28 with language impairment) yielded 9,084 utterances that were DSS ... Research Article
Research Article  |   August 01, 2003
Automated Developmental Sentence Scoring Using Computerized Profiling Software
 
Author Affiliations & Notes
  • Ron W. Channell, PhD
    Brigham Young University, Provo, UT
  • Contact author: Ron W. Channell, PhD, 128 TLRB, Brigham Young University, Provo, UT 84602.
    Contact author: Ron W. Channell, PhD, 128 TLRB, Brigham Young University, Provo, UT 84602.×
  • Corresponding author: E-mail: channellr@byu.edu
Article Information
Research Articles
Research Article   |   August 01, 2003
Automated Developmental Sentence Scoring Using Computerized Profiling Software
American Journal of Speech-Language Pathology, August 2003, Vol. 12, 369-375. doi:10.1044/1058-0360(2003/082)
History: Received April 18, 2002 , Accepted February 14, 2003
 
American Journal of Speech-Language Pathology, August 2003, Vol. 12, 369-375. doi:10.1044/1058-0360(2003/082)
History: Received April 18, 2002; Accepted February 14, 2003

This study examined the accuracy of fully automated Developmental Sentence Scoring (DSS; L. L. Lee, 1974) analysis performed by the Computerized Profiling (CP) software (S. H. Long, M. E. Fey, & R. W. Channell, 2000). Samples from 48 school-age children (28 with language impairment) yielded 9,084 utterances that were DSS coded both manually and by CP. The point-by-point agreement of CP with manual coding was 78%, with per-category levels of agreement ranging from 0% to 98%. Agreement levels were about 2% lower on samples from children with language impairment. Though significantly higher, the scores computed by CP were highly correlated (r = .97) with the manually computed scores. Further work on improving the accuracy of automated DSS analysis is warranted.

Acknowledgments
I am grateful to Bonnie Brinton, Martin Fujiki, and Jayna Collingridge for access to language samples, to Natalie Pope for help in processing the samples, and to Susan Grenfell and Lisa Boyce for their piloting thesis work in this topical area.
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