Computerizing reading training: Evaluation of a latent semantic analysis space for science text

Christopher A. Kurby, Katja Wiembr-Hastings, Nagasai Ganduri, Joseph P. Magliano, Keith K. Millis, Danielle S. McNamara

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


The effectiveness of a domain-specific latent semantic analysis (LSA) in assessing reading strategies was examined. Students were given self-explanation reading training (SERT) and asked to think aloud after each sentence in a science text. Novice and expert human raters and two LSA spaces (general reading, science) rated the similarity of each think-aloud protocol to benchmarks representing three different reading strategies (minimal, local, and global). The science LSA space correlated highly with human judgments, and more highly than did the general reading space. Also, cosines from the science LSA spaces can distinguish between different levels of semantic similarity, but may have trouble in distinguishing local processing protocols. Thus, a domain-specific LSA space is advantageous regardless of the size of the space. The results are discussed in the context of applying the science LSA to a computer-based version of SERT that gives online feedback based on LSA cosines.

Original languageEnglish (US)
Pages (from-to)244-250
Number of pages7
JournalBehavior Research Methods, Instruments, and Computers
Issue number2
StatePublished - May 2003
Externally publishedYes

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Psychology (miscellaneous)
  • Psychology(all)


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