Less users more confidence: How AOIs don’t affect scanpath trend analysis

Author:

Eraslan SukruORCID,Yesilada YelizORCID,Harper SimonORCID

Abstract

User studies are typically difficult, recruiting enough users is often problematic and each experiment takes a considerable amount of time to be completed. In these studies, eye tracking is increasingly used which often increases time, therefore, the lower the number of users required for these studies the better for making these kinds of studies more practical in terms of economics and time expended. The possibility of achieving almost the same results with fewer users has already been raised. Specifically, the possibility of achieving 75% similarity to the results of 65 users with 27 users for searching tasks and 34 users for browsing tasks has been observed in scanpath trend analysis which discovers the most commonly followed path on a particular web page in terms of its visual elements or areas of interest (AOIs). Different approaches are available to segment or divide web pages into their visual elements or AOIs. In this paper, we investigate whether the possibility raised by the previous work is restricted to a particular page segmentation approach by replicating the experiments with two other segmentation approaches. The results are consistent with ~5% difference for the searching tasks and ~10% difference for the browsing tasks.

Publisher

University of Bern

Subject

Sensory Systems,Ophthalmology

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1. Predicting Trending Elements on Web Pages Using Machine Learning;International Journal of Human–Computer Interaction;2023-10-02

2. Automatically Classifying Familiar Web Users From Eye-tracking Data: A Machine Learning Approach;TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES;2021

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