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2011
ACM

Identifying enrichment candidates in textbooks

8 years 6 months ago
Identifying enrichment candidates in textbooks
Many textbooks written in emerging countries lack clear and adequate coverage of important concepts. We propose a technological solution for algorithmically identifying those sections of a book that are not well written and could benefit from better exposition. We provide a decision model based on the syntactic complexity of writing and the dispersion of key concepts. The model parameters are learned using a tune set which is algorithmically generated using a versioned authoritative web resource as a proxy. We evaluate the proposed methodology over a corpus of Indian textbooks which demonstrates its effectiveness in identifying enrichment candidates. Categories and Subject Descriptors H.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Algorithms, Experimentation, Human Factors Keywords Education, Textbooks, Readability, Concepts, Dispersion
Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where WWW
Authors Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi
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