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

• Type: Journal
• Year: 2011
• Where: WWW
• Authors: Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi
• Keywords: Human Factors Keywords, Internet Technology, Retrieval General Terms, Versioned Authoritative Web, WWW 2011