EAGER: Machine learning of discourse structure for personalized online tutoring

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Technology in education provides both tremendous opportunities and tremendous challenges.  By delivering instruction online, we may be able to reach a much wider range of students than previously possible.  But, while technology allows instruction to reach more students, it also risks losing the rich interaction and feedback that in-person instruction provides.  To be truly effective, courses need both rich content and personalized interaction and feedback. So, this project seeks to provide a technical foundation for restoring richer interaction to the online learning experience, particularly through natural language content such as discussion boards and peer grading. The project will develop machine learning techniques for discovering high-level structure in natural language text, including discourse structure (relationships among pieces of text), topic structure, and semantic structure.  To discover this structure, the project investigates the use of spectral learning methods.  These methods, which rely on factoring a matrix or tensor of observed moments, are able to learn latent structures efficiently and without local optima.  Of particular interest and challenge is to develop tractable methods that can learn latent structure from unlabeled or weakly-labeled data.  Many current NLP techniques have difficulty with informal language and do not extract higher level structure; our goal is to develop spectral methods to address these weaknesses. The project will attempt to learn this high-level latent structure in data taken from discussion boards of large online classes.  The idea is that future work could use this understanding to help personalize the way students access this sort of rich natural language content: e.g., by helping to focus searches, find relevant and cohesive content, synthesize answers to student questions, promote productive behaviors in online discussions, and support self and peer grading.  The eventual goal is to provide personalized interfaces to students that facilitate their learning of novel material.