Digital reading applications give readers the ability to customize fonts, sizes, and spacings, all of which have been shown to improve various aspects of reading, including reading comfort, comprehension, and speed. However, tweaking these text features can be challenging, especially given their interactions on the final look and feel of the text. Our solution is to offer readers preset combinations of font, character, word and line spacing, which we bundle together into reading themes. To arrive at a recommended set of reading themes, we present our THERIF framework, which combines crowdsourced text adjustments, ML-driven clustering of text formats, and design sessions. We show that after four iterations of our framework, we converge on a set of three COR themes (Compact, Open, and Relaxed) that match diverse readers' needs, when evaluating the reading speeds, comprehension scores, and preferences of hundreds of readers with and without dyslexia, using crowdsourced experiments.
Find text settings that matter
In pilot study, participants had access to a wide range of controls for tailoring reading interfaces to their preference.
Rate and refine
In the main study, participants review themes from the previous iteration and select one to fine-tune further.
Refine and supplement
Designers use the Participant Prototype to review the themes and use the Designer Prototype to create supplementary reading themes.
Speed, comprehension, comfort
A separate group of participants read in all final reading themes and a control theme, their speed, comprehension, and comfort ratings recorded.