Readability Research: An Interdisciplinary Approach

Beier, Sofie; Berlow, Sam; Esat, Boucaud,; Bylinskii, Zoya; Cai, Tianyuan; Cohn, J. H. E.; Crowley, Kathy; Day, Stephanie L.; Dingler, Tilman; Dobres, Jonathan; Healey, Jennifer; Jain, Rajiv; Jordan, Marjorie; Kerr, Bernard J; Li, Qisheng; Miller, David; Nobles, Susanne; Papoutsaki, Alexandra; Jiang, Qian; Rezvanian, Tina; Rodrigo, Shelley; Sawyer, Ben D.; Stein, Bram; Treitman, Rick; Vanek, Jen; Wallace, Shaun; Wolfe, Benjamin

doi:10.48550/arxiv.2107.09615

Cited by 5 publications

(6 citation statements)

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“…While some segments of the population tend to get more benefits from the reading themes (e.g., older participants and those who score higher on the dyslexia questionnaire both preferred, and performed better in, the themes with larger spacing), the themes are intended to be universal. Another way to look at these results is that while the effects of text settings on reading may be governed by some demographic characteristics, reading preferences and performances are highly variable across individuals, as found also by prior work (Beier et al, 2021a;Cai et al, 2022;Chatrangsan and Petrie, 2019;Zhu et al, 2021;McKoon and Ratcliff, 2016;Korinth et al, 2020).…”

Section: Speedmentioning

confidence: 84%

THERIF: A Pipeline for Generating Themes for Readability with Iterative Feedback

Cai¹,

Niklaus²,

Kraley³

et al. 2023

Preprint

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Digital reading applications give readers the ability to customize fonts, sizes, and spacings, all of which have been shown to improve the reading experience for readers from different demographics. 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 pipeline, which combines crowdsourced text adjustments, ML-driven clustering of text formats, and design sessions. We show that after four iterations of our pipeline, we converge on a set of three COR themes (Compact, Open, and Relaxed) that meet diverse readers' preferences, when evaluating the reading speeds, comprehension scores, and preferences of hundreds of readers with and without dyslexia, using crowdsourced experiments.

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Section: Speedmentioning

confidence: 84%

THERIF: A Pipeline for Generating Themes for Readability with Iterative Feedback

Cai¹,

Niklaus²,

Kraley³

et al. 2023

Preprint

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“…Our study features a pre-survey, study overview instructions, a practice round 8 , the main study, and a post-survey. The main study includes an instruction screen and a font preference test, followed by another instruction screen and reading speed and comprehension tests per font.…”

Section: Study Methodsmentioning

confidence: 99%

“…From our interview with typographers and past work, we learned that the metrics contained in font fles, the OS/2 tables, often do not accurately refect font characteristics (P3, P5, [8]). Therefore, we developed a web application, FontView, to quantify the typographical features discussed during the interviews.…”

Section: Fontview: Extracting Font Characteristicsmentioning

confidence: 99%

“…Reading Materials: To add control to our study, we used reading passages in English normed to an 8th-grade reading level [8,90]. Prior work shows these 8th-grade reading passages are easy enough to allow the reader to read faster without reducing comprehension [90].…”

Section: Readersmentioning

confidence: 99%

“…Prior work shows these 8th-grade reading passages are easy enough to allow the reader to read faster without reducing comprehension [90]. Future studies may consider optimizing for multiple objectives, such as the speed and comprehension trade-of [8,91] by presenting passages with multiple levels of difculty. Future work is also needed to evaluate the model's efectiveness for other types of reading activities, such as long-form reading or skimming.…”

Section: Readersmentioning

confidence: 99%

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Personalized Font Recommendations: Combining ML and Typographic Guidelines to Optimize Readability

Cai

Wallace

Rezvanian

et al. 2022

Designing Interactive Systems Conference

Self Cite

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The amount of text people need to read and understand grows daily. Software defaults, designers, or publishers often choose the fonts people read in. However, matching individuals with a faster font could help them cope with information overload. We collaborated with typographers to (1) select eight fonts designed for digital reading to systematically compare their efectiveness and to (2) understand how font and reader characteristics afect reading speed. We collected font preferences, reading speeds, and characteristics from 252 crowdsourced participants in a remote readability study. We use font and reader characteristics to train FontMART, a learning to rank model that automatically orders a set of eight fonts per participant by predicted reading speed. FontMART's fastest font prediction shows an average increase of 14-25 WPM compared to other font defaults, without hindering comprehension. This encouraging evidence provides motivation for adding our personalized font recommendation to future interactive systems.

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Reading textual and non-textual explanations in chemistry texts and textbooks – a review

Meyer

Pietzner

2022

Chem. Educ. Res. Pract.

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Readability Research: An Interdisciplinary Approach

Cited by 5 publications

References 0 publications

THERIF: A Pipeline for Generating Themes for Readability with Iterative Feedback

THERIF: A Pipeline for Generating Themes for Readability with Iterative Feedback

Personalized Font Recommendations: Combining ML and Typographic Guidelines to Optimize Readability

Reading textual and non-textual explanations in chemistry texts and textbooks – a review

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