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

Christopher A Kurby·Katja Wiemer-Hastings·Nagasai Ganduri·Joseph P Magliano·Keith K Millis·Danielle S McNamara
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Abstract

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.

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Artificial IntelligenceComputer-Assisted InstructionEvaluation Studies as TopicHumansLearningMental ProcessesModels, PsychologicalReadingScienceSemanticsSoftware ValidationComparative StudyEvaluation StudyJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

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Christopher A Kurby, Katja Wiemer-Hastings, Nagasai Ganduri, Joseph P Magliano, Keith K Millis, Danielle S McNamara. 2003. Computerizing reading training: evaluation of a latent semantic analysis space for science text.. https://doi.org/10.3758/bf03202547

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