Functional Analytics for Document Ordering for Curriculum Development and Comprehension
This addresses the need for automated content sequencing in education and training, though it is incremental as it builds on existing techniques like LDA and similarity measures.
The paper tackled the problem of automatically generating document orders for curriculum development and optimal reading sequences, finding that their methods based on document similarity and topic entropy worked reliably on textbooks, courses, and academic papers but not on control sets like biographies or novels, and that summarized documents were effective substitutes for full documents in ordering.
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.