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.

identifyingEnrichmentCandidatesInTextbooks-www2011.pdf
PDF file

In  International World Wide Web Conference (WWW)

Publisher  ACM
Copyright © 2009 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. The definitive version of this paper can be found at ACM’s Digital Library --http://www.acm.org/dl/.

Details

TypeInproceedings
> Publications > Identifying Enrichment Candidates in Textbooks