Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Predicting readability of data processing written materials
ACM SIGMIS Database
Measuring article quality in wikipedia: models and evaluation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Predicting the readability of short web summaries
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Domain-specific iterative readability computation
Proceedings of the 10th annual joint conference on Digital libraries
Learning to predict readability using diverse linguistic features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Enriching textbooks through data mining
Proceedings of the First ACM Symposium on Computing for Development
Evaluating facilitated video instruction for primary schools in rural India
Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development
Enriching education through data mining
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Enriching education through data mining
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Enriching textbooks with images
Proceedings of the 20th ACM international conference on Information and knowledge management
On the feasibility and utility of web based educational lesson plans
Proceedings of the 2nd ACM Symposium on Computing for Development
Data mining for improving textbooks
ACM SIGKDD Explorations Newsletter
Empowering authors to diagnose comprehension burden in textbooks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Textbooks for developing regions
Proceedings of the first workshop on Information and knowledge management for developing region
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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.