Investigating user behavior by analysis of gaze data : Evaluation of machine learning methods for user behavior analysis in web applications

Abstract: User behavior analysis in web applications is currently mainly performed by analysis of statistical measurements based on user interactions or by creation of personas to better understand users. Both of these methods give great insights in how the users utilize a web site, but do not give any additional information about what they are actually doing. This thesis attempts to use eye tracking data for analysis of user activities in web applications. Eye tracking data has been recorded, labeled and analyzed for 25 test participants. No data source except eye tracking data has been used and two different approaches are attempted where the first relies on a gaze map representation of the data and the second relies on sequences of features. The results indicate that it is possible to distinguish user activities in web applications, but only at a high error-rate. Improvement are possible by implementing a less subjective labeling process and by including features from other data sources.