EAGER: Predicting Domain-level Reading Comprehension Difficulty to Support Adult Learning

University Of Pennsylvania, Philadelphia PA

Investigators

Abstract

Regardless of their level of education, participants in the modern workforce are expected to be flexible in their ability to read and learn in new, often technical domains, including scientific subfields, medicine, policy, and care. Remarkably, however, there is little, if any, technological support for such learning, which often is dynamic and requires reading texts as needed rather than in an ordered curriculum as in classroom learning. This Early Grant for Exploratory Research addresses the need for technological support for adult learning in technical domains by exploring the feasibility of simulating the behavior of an expert reader in identifying important content and drawing inferences to connect rich background knowledge and the text at hand. Readers will be provided access to pilot implementations of the inferences and importance judgements of the simulated expert, to focus their attention and strengthen their comprehension of the text. This development of exploratory models of expert reader behavior relies on techniques for characterizing text vocabulary into technical and plain language by contrasting word occurrence statistics in the domain and in general text such as in a random sample of telephone conversations or news. It will also explore the adaptation of techniques for definition mining and for deriving prototypical event sequences and shallow ontologies from large volume of typical domain text.