Transforming digital reading with visual-syntactic text formatting

Abstract

Visual-syntactic text formatting uses natural language processing techniques to parse sentences and present them in a way that highlights meaning. Specifically, VSTF breaks sentences up at salient clause and phrase boundaries, fits each row of text into one or two fixation eyespans, uses a cascading pattern to denote syntactic hierarchies, and creates visual clusters across multiple rows to help readers retain and integrate multi-phrase images. This paper reviews the theoretical basis of VSTF and summarizes research on its impact on reading comprehension, speed, retention, and proficiency, with a focus on its use by English language learners.