An Exploration of Cursor tracking Data
This work explores cursor data for web analytics and user understanding, but is incremental in applying existing tracking methods to new tasks.
The study used cursor tracking data from Wikipedia and BBC News tasks to differentiate between reading and information-seeking users, and identified hardware from cursor data but found no relationship with engagement.
Cursor tracking data contains information about website visitors which may provide new ways to understand visitors and their needs. This paper presents an Amazon Mechanical Turk study where participants were tracked as they used modified variants of the Wikipedia and BBC News websites. Participants were asked to complete reading and information-finding tasks. The results showed that it was possible to differentiate between users reading content and users looking for information based on cursor data. The effects of website aesthetics, user interest and cursor hardware were also analysed which showed it was possible to identify hardware from cursor data, but no relationship between cursor data and engagement was found. The implications of these results, from the impact on web analytics to the design of experiments to assess user engagement, are discussed.