Functional Analytics for Document Ordering for Curriculum Development
  and Comprehension
Arturo N. Villanueva Jr. Steven J. Simske
We propose multiple techniques for automatic document order generation for
(1) curriculum development and for (2) creation of optimal reading order for
use in learning, training, and other content-sequencing applications. Such
techniques could potentially be used to improve comprehension, identify areas
that need expounding, generate curricula, and improve search engine results. We
advance two main techniques: The first uses document similarities through
various methods. The second uses entropy against the backdrop of topics
generated through Latent Dirichlet Allocation (LDA). In addition, we try the
same methods on the summarized documents and compare them against the results
obtained using the complete documents. Our results showed that while the
document orders for our control document sets (biographies, novels, and
Wikipedia articles) could not be predicted using our methods, our test
documents (textbooks, courses, journal papers, dissertations) provided more
reliability. We also demonstrated that summarized documents were good stand-ins
for the complete documents for the purposes of ordering.