Less Users More Confidence: How AOIs Don't Affect Scanpath Trend Analysis.

Area of Science:

• Human-Computer Interaction
• Usability Engineering
• Cognitive Science

Background:

• User studies are time-consuming and costly, especially with eye-tracking.
• Recruiting sufficient participants for eye-tracking research presents significant challenges.
• Reducing participant numbers can enhance the practicality and economic viability of user studies.

Purpose of the Study:

• To investigate if previous findings on reducing user numbers in eye-tracking studies are dependent on specific web page segmentation methods.
• To validate the generalizability of scanpath trend analysis with fewer participants across different segmentation approaches.

Main Methods:

• Replication of previous experiments using two additional web page segmentation approaches.
• Analysis of scanpath trends to identify common user navigation paths.
• Comparison of results obtained with reduced participant numbers (27 for search, 34 for browse) against a larger group (65 users).

Main Results:

• Scanpath trend analysis with fewer users achieved high similarity to results from larger user groups.
• Approximately 75% similarity was observed for searching tasks and similar levels for browsing tasks.
• Results remained consistent across different page segmentation methods, with minor differences (~5% for search, ~10% for browse).

Conclusions:

• The findings suggest that a reduced number of participants can yield reliable results in eye-tracking studies using scanpath trend analysis.
• The generalizability of these findings across different web page segmentation approaches is supported.
• This research contributes to making eye-tracking user studies more efficient and cost-effective.