Integrating Click-Through and Eye-Tracking Logs for Decision-Making Process Mining

In current software every click of the users is logged, therefore a wealth of click-through information exists. Besides, recent technologies have made eye-tracking affordable and an alternative to other human-computer interaction means (e.g. mouse, touchscreens). A big challenge is to make sense of all this data and convert it into useful information. This paper introduces a possible solution placed in the context of decision-making processes. We show how the decision maker's activity can be traced using two means: mouse tracing (i.e. clicks) and eye-tracking (i.e. eye fixations). Then, we discuss a mining approach, based on the log, which extracts a Decision Data Model (DDM). We use the DDM to determine, post-hoc, which decision strategy was employed. The paper concludes with a validation based on a controlled experiment.