期刊名称：AIS Transactions on Human-Computer Interaction
印刷版ISSN：1944-3900
出版年度：2018
卷号：10
期号：1
页码：26-56
出版社：Association for Information Systems
摘要：We propose a method to predict user performance based on eye-tracking. The method uses eye-tracking-based pupillometry to capture pupil diameter data and calculates—based on a Random Forest algorithm—user performance expectations. We conducted a large-scale experimental evaluation (125 participants aged from 21 to 61 years) and found promising results that pave the way for a dynamic real-time adaption of IT to a user’s mental effort and expected user performance. We have already achieved a good classification accuracy of user performance after only 40 seconds (5% of the mean total trial time that our participants took to complete our experiment). The non-invasive contact-free method can be applied cost-efficiently both in research and practical environments.
关键词：NeuroIS; user performance; mental effort; pupillometry; eye-tracking; Random Forest
