Mitigating the Effects of Reading Interruptions by Providing Reviews and Previews

Srivastava, Namrata; Jain, Rajiv; Healey, Jennifer; Bylinskii, Zoya; Dingler, Tilman

doi:10.1145/3411763.3451610

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…The graded labeling scheme provides the model with granular information about the relationship between fonts. However, uncontrollable environmental distractions during the remote readability tests may introduce noise at the individual participant level [83]. On the other hand, using binary labels may mitigate the model's tendency to overft the noise on this relatively small dataset.…”

Section: Model Designmentioning

confidence: 99%

“…Environmental Distractions: When participating in remote studies, it is not uncommon for participants to encounter distractions such as notifcations on their computer screen, phone calls, or caring for family members [83]. While our study design does not directly control for external distractions, readers could take breaks before and after reading passages.…”

Section: A Additional Limitationsmentioning

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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Section: Model Designmentioning

confidence: 99%

Section: A Additional Limitationsmentioning

confidence: 99%

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

Cai

Wallace

Rezvanian

et al. 2022

Designing Interactive Systems Conference

Self Cite

View full text Add to dashboard Cite

show abstract

Towards Individuated Reading Experiences: Different Fonts Increase Reading Speed for Different Individuals

Wallace

Bylinskii

Dobres

et al. 2022

ACM Trans. Comput.-Hum. Interact.

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In our age of ubiquitous digital displays, adults often read in short, opportunistic interludes. In this context of Interlude Reading , we consider if manipulating font choice can improve adult readers’ reading outcomes. Our studies normalize font size by human perception and use hundreds of crowdsourced participants to provide a foundation for understanding, which fonts people prefer and which fonts make them more effective readers. Participants’ reading speeds (measured in words-per-minute (WPM)) increased by 35% when comparing fastest and slowest fonts without affecting reading comprehension. High WPM variability across fonts suggests that one font does not fit all. We provide font recommendations related to higher reading speed and discuss the need for individuation, allowing digital devices to match their readers’ needs in the moment. We provide recommendations from one of the most significant online reading efforts to date. To complement this, we release our materials and tools with this article.

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Supporting Task Switching with Reinforcement Learning

Lingler,

Talypova,

Jokinen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Mitigating the Effects of Reading Interruptions by Providing Reviews and Previews

Abstract: Figure 1: An example of a review and a preview used in our study for mitigating the effects of reading interruption.

Cited by 6 publications

References 21 publications

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

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

Towards Individuated Reading Experiences: Different Fonts Increase Reading Speed for Different Individuals

Supporting Task Switching with Reinforcement Learning

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