June 01, 2022

KD2School Goes to NeuroIS

With June we see the first acceptance series of conference articles by several KD2School members and associates. At the 12th annual retreat on neuro-information systems (NeuroIS), an international group of scholars is gathering to discuss conceptual and empirical works, as well a theoretical and design science research. Thereby, NeuroIS work includes research based on all types of neuroscience and physiological methods. With numerous contributions we will participate in this timely discussion revolving around how adaptive systems can influence economic decision making and everyday experience. The submissions to be presented are:

  • Seitz, J., and Maedche, A. (2022) Biosignal-based Recognition of Cognitive Load: A Systematic Review of Public Datasets and Classifiers.
  • Langner, M. et al. (2022) Recognizing Polychronic-Monochronic Tendency of Individuals using Eye Tracking and Machine Learning.
  • Bartholomeyczik, K. et al. (2022) Flow in Knowledge Work: An Initial Evaluation of Flow Psychophysiology Across Three Cognitive Tasks.
  • Nikolajevic, N., et al. (2022). Facilitating NeuroIS Research using Natural Language Processing: Towards Automated Recommendations.
  • Greif-Winzrieth, A., et al. (2022) A Participant’s View on the Potential of Conducting NeuroIS Studies in the Wild.
January 23, 2022

Designing Attentive Information Dashboards

Toreini et al. 2021 - Published in Journal of the Association for Information Systems

Information dashboards are a critical capability in contemporary business intelligence & analytics systems. Despite coming with strong potential to support better decision making, the huge amount of information provided challenges their users when they perform data exploration tasks. Accordingly, dashboard users face difficulties in managing their limited attentional resources when processing the presented information on dashboards. Also, recent studies show that the amount of concentrated time humans can spend on a task is reduced massively and there is a need for designing user interfaces that support their users' attention management. Therefore, in this design science research project, we propose attentive information dashboards that provide individualized visual attention feedback (VAF) as an innovative artifact to solve this problem. We articulate theoretically grounded design principles and instantiate a software artifact leveraging users' eye movement gaze data in real-time to provide individualized VAF. The instantiated artifact was evaluated in a controlled lab experiment with 92 participants.

The results from analyzing users' eye movement after receiving individualized VAF reveal that our proposed design has a positive effect on users' attentional resource allocation, attention shift rate, and attentional resource management. We contribute a system architecture for attentive information dashboards that support data exploration and two theoretically grounded design principles that provide prescriptive knowledge on how to provide individualized VAF. Further, practitioners can leverage the prescriptive knowledge derived from our research and design innovative systems that support users' information processing by managing their limited attentional resources.

January 18, 2022

Towards a Physiological Computing Infrastructure for Researching Students’ Flow in Remote Learning – Preliminary Results from a Field Study.

Li et al. 2021 - Published in Technology, Knowledge and Learning

With the advent of physiological computing systems, new avenues are emerging for the field of learning analytics related to the potential integration of physiological data. To this end, we developed a physiological computing infrastructure to collect physiological data, surveys, and browsing behavior data to capture students’ learning journey in remote learning. Specifically, our solution is based on the Raspberry Pi minicomputer and Polar H10 chest belt.

In this work-in-progress paper, we present preliminary results and experiences we collected from a field study with medical students using our developed infrastructure. Our results do not only provide a new direction for more effectively capturing different types of data in remote learning by addressing the underlying challenges of remote setups, but also serve as a foundation for future work on developing a less obtrusive, (near) real-time measurement method based on the classification of cognitive-affective states such as flow or other learning-relevant constructs with the captured data using supervised machine learning.

December 9, 2021

LeadBoSki: A Smart Personal Assistant for Leadership Support in Video-Meetings

Benke et al. 2021 - Published at CSCW

Leading teams effectively is an essential factor for team performance. Main events where leaders practice leadership are video-meetings which are core elements of remote teamwork. However, leading teams is challenging. In particular, in video-meetings, leadership is complex due to limitations such as aggravated understanding of social signals and team dynamics.

Voice-based smart personal assistants (SPA) such as Amazon’s Alexa can provide real-time support and have the potential to maintain effective leadership in video-meetings. Therefore, we developed the SPA LeadBoSki for real-time leadership support in video-meetings. LeadBoSki detects leadership potentials in video-meetings and sends leadership potential advice to the leader. Pilot study results with two real-world teams (n=9) show a positive leader experience and stimulating behavior by the leader for the team members’ motivation. Our work contributes with the SPA LeadBoSki as the first SPA for leadership support in video-meetings of its kind and as a potential use-case for future workplaces.