Running head: LEARNING FROM TEXT
Learning from text:
Matching readers and texts by Latent Semantic Analysis

Michael B. W. Wolfe, M. E. Schreiner, Bob Rehder, Darrell Laham,
University of Colorado, Boulder
Peter W. Foltz
New Mexico State University
Walter Kintsch, and Thomas K Landauer
University of Colorado, Boulder
Wolfe, M. B., Schreiner, M. E., Rehder, B., Laham, D.,
Foltz, P. W., Kintsch, W., & Landauer, T. K. (1998).
Learning from text: Matching readers and text by Latent
Semantic Analysis. Discourse Processes, 25, 309336.
Correspondence to:
Michael B.W. Wolfe
Campus Box 345  Cognitive
University of Colorado
Boulder, CO 80309
(303) 4927299
Email: wolfem@psych.colorado.edu

Abstract
This study examines the hypothesis that the ability of a reader to learn
from text depends on the match between the background knowledge of
the reader and the difficulty of the text information. Latent Semantic
Analysis (LSA), a statistical technique that represents the content of a
document as a vector in high dimensional semantic space based on a
large text corpus, is used to predict how much readers will learn from
texts based on the estimated conceptual match between their topic
knowledge and the text information. Participants completed tests to
assess their knowledge of the human heart and circulatory system,
then read one of four texts that ranged in difficulty from elementary to
medical school level, then completed the tests again. Results show a
nonmonotonic relationship in which learning was greatest for texts
that were neither too easy nor too difficult. LSA proved as effective at
predicting learning from these texts as traditional knowledge
assessment measures. For these texts, optimal assignment of text on the
basis of either prereading measure would have increased the amount
learned significantly.



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