Abstract

iSTART is a web-based reading comprehension tutor. A recent translation of iSTART from English to Spanish has made the system available to a new audience. In this paper, we outline several challenges that arose during the development process, specifically focusing on the algorithms that drive the feedback. Several iSTART activities encourage students to use comprehension strategies to generate self-explanations in response to challenging texts. Unsurprisingly, analyzing responses in a new language required many changes, such as implementing Spanish natural language processing tools and rebuilding lists of regular expressions used to flag responses. We also describe our use of an algorithm inspired from genetics to optimize the Fischer Discriminant Function Analysis coefficients used to determine self-explanation scores.