AttentionBoard: A Quantified-Self Dashboard for Enhancing Attention Management with Eye-Tracking

by | Oct 27, 2020 | Research Publications

Author(s):

  • Moritz Langner
  • Peyman Toreini
  • Alexander Maedche

Abstract:

In the age of information, office workers process huge amounts of information and distribute their attention to several tasks in parallel. However, attention is a scarce resource and attentional breakdowns, such as missing important information, may occur while using information systems (IS). Currently, there is a lack of support to understand and improve attention management to avoid such breakdowns. In the meantime, self-tracking applications are becoming popular due to the increasing sensory capabilities of smart devices. These systems support their users in understanding and reflecting their behavior. In this research-in-progress paper, we suggest leveraging self-tracking concepts for attention management while working with ISs and describe the design of the NeuroIS-based system called “AttentionBoard”. The goal of AttentionBoard is to help office workers in improving their attention management competencies. The system records attention allocation in real-time using eye-tracking and presents the aggregated data as metrics and visualizations on a dashboard. This paper presents the first step by motivating and introducing an initial design following the design science research (DSR) methodology.

Documentation:

https://doi.org/10.1007/978-3-030-60073-0_31

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