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We propose multiple techniques for automatic document order generation for(1) curriculum development and for (2) creation of optimal reading order foruse in learning, training, and other content-sequencing applications. Suchtechniques could potentially be used to improve comprehension, identify areasthat need expounding, generate curricula, and improve search engine results. Weadvance two main techniques: The first uses document similarities throughvarious methods. The second uses entropy against the backdrop of topicsgenerated through Latent Dirichlet Allocation (LDA). In addition, we try thesame methods on the summarized documents and compare them against the resultsobtained using the complete documents. Our results showed that while thedocument orders for our control document sets (biographies, novels, andWikipedia articles) could not be predicted using our methods, our testdocuments (textbooks, courses, journal papers, dissertations) provided morereliability. We also demonstrated that summarized documents were good stand-insfor the complete documents for the purposes of ordering.

Functional Analytics for Document Ordering for Curriculum Development and Comprehension