Characteristics of Deep and Skim Reading on Smartphones vs. Desktop: A Comparative Study


Deep reading fosters text comprehension, memory, and critical thinking. The growing prevalance of digital reading on mobile interfaces raises concerns that deep reading is being replaced by skimming and sifting through information, but this is currently unmeasured. Traditionally, reading quality is assessed using comprehension tests, which require readers to explicitly answer a set of carefully composed questions. To quantify and understand reading behaviour in natural settings and at scale, however, implicit measures are needed of deep versus skim reading across desktop and mobile devices, the most prominent digital reading platforms. In this paper, we present an approach to systematically induce deep and skim reading and subsequently train classifiers to discriminate these two reading styles based on eye movement patterns and interaction data. Based on a user study with 29 participants, we created models that detect deep reading on both devices with up to 0.82 AUC. We present the characteristics of deep reading and discuss how our models can be used to measure the effect of reading UI design and monitor long-term changes in reading behaviours.

Xiuge Chen
The University of Melbourne, Melbourne, Victoria, Australia
Namrata Srivastava
Monash University, Melbourne, Victoria, Australia
Rajiv Jain
Adobe Research, College Park, Maryland, United States
Jennifer Healey
Adobe Research, San Jose, California, United States
Tilman Dingler
University of Melbourne, Melbourne, Victoria, Australia


会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Modalities

Hall F
5 件の発表
2023-04-25 01:35:00
2023-04-25 03:00:00