Cursor movement and user engagement measurement

Many researchers have argued that cursor tracking data can provide enhance ways to learn about website visitors. One of the most difficult website performance metrics to accurately measure is user engagement, generally defined as the amount of attention and time visitors are willing to spend on a given website and how likely they are to return. Engagement is usually described as a combination of various characteristics. Many of these are difficult to measure, for example, focused attention and affect. These would traditionally be measured using physiological sensors (e.g. gaze tracking) or surveys. However, it may be possible that this information could be gathered through an analysis of cursor data.

This work [1] presents a study that asked participants to complete tasks on live websites using their own hardware in their natural environment. For each website two interfaces were created: one that would appear as normal and one that was intended to be aesthetically unappealing, as shown below. The participants, who were recruited through a crowd-sourcing platform, were tracked as they used modified variants of the Wikipedia and BBC News websites. There were asked to complete reading and information-finding tasks.

wiki_normal wiki_ugly

 

 

 

 

 

 

 

The aim of the study was to explore how cursor tracking data might tell us more about the user than could be measured using traditional means. The study explored several metrics that might be used when carrying out cursor tracking analyses. The results showed that it was possible to differentiate between users reading content and users looking for information based on cursor data. They also showed that the user’s hardware could be predicted from cursor movements alone. However, 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.

This study demonstrates that designing experiments to obtain reliable insights about user engagement and its measurement remains challenging. Not finding a signal may not necessary means that the signal does not exist, but that some of the metrics used were not the correct ones. In hindsight, this is what we believe happened. The cursor metrics were not the right ones to differentiate between the levels of engagement experience as examined in this work. Indeed, recent work [2] showed that more complex mouse movement metrics did correlate with some engagement metrics.

  1. David Warnock and Mounia Lalmas. An Exploration of Cursor tracking Data. ArXiv e-prints, February 2015.
  2. Ioannis Arapakis, Mounia Lalmas Lalmas and George Valkanas. Understanding Within-Content Engagement through Pattern Analysis of Mouse Gestures, 23rd International Conference on Information and Knowledge Management (CIKM), November 2014.

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