This project's goal is to examine how reader and text characteristics combine to influence the comprehension of science texts. The study will make use of recent advances in psychometrics on hierarchical linear models in order to assess the predictive values of a large number of linguistic properties, reader characteristics, and the structure of those factors. Through analyses of individual differences in readers and analyses of text difficulty, the researchers hope to be able to make progress toward using computer systems to customized complex texts to individual readers. This, they hope, would make such texts generally readable to a heterogeneous group of readers.
The intellectual merit of the project is in the multidisciplinary approach that the researchers adopt in order to address problems in reading comprehension. Bringing psychometric tools into contact with sentence processing paradigms from cognitive science has the potential to yield new findings of interest to the scientific community.
The broader impact of the study if successful is that more people would be able to read and understand science. The development of diagnostic test to measure the required reading skills for complex texts would also be beneficial.
