NDLI: An automatic text comprehension classifier based on mental models and latent semantic features

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An automatic text comprehension classifier based on mental models and latent semantic features

Content Provider	ACM Digital Library
Author	Ríos, Sebastián A. L'Huillier, Gaston Moya, Patricio Bravo-Marquez, Felipe Velásquez, Juan D.
Abstract	Reading comprehension is one of the main concerns for educational institutions, as it forges the students' ability to comprehend and learn accurately a given information source (e.g. textbooks, articles, papers, etc.). However, there are few approaches that integrates digital sources of educational information with automated systems to detect whether an individual has comprehended a given reading task. This work main contribution is a text comprehension classification methodology for the detection of reading comprehension failures in educational institutions. The proposed approach relates situational model theories and latent semantic analysis from fields of psycholinguistics and natural language processing respectively. A numerical characterization of students' documents using structural information, such as the usage of text connectors, and latent semantic features are used as input for traditional classification algorithms. Therefore, an automated classifier is built to determine whether a given student could or not comprehend the information in the given stimulus documents. For the evaluation of the proposed methodology, using a set of stimulus documents, a set of questions must be answered by an experimental group of students. We have performed experiments using first year students from Engineering and Linguistics undergraduate schools at the University of Chile with promising results.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450307321
DOI	10.1145/2024288.2024317
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-09-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text comprehension Text mining Text comprehension evaluation Situational models Classification Latent semantic analysis
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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