Challenge
Data science is redefining organizational decision-making in most areas of our lives; yet this transformation is fraught −especially in management − with noteworthy methodological complexity. Traditional data science educational offerings introduce inherent difficulties to students that impede their learning objectives, through limited capabilities to reduce complexity through mathematical constructs only the low level of interaction during the learning experience, as well as delayed feedback (this stands in stark contrast to case-based learning), the difficulty in finding an entry point to data science that is properly balanced with their educational background(s), and
Approach
This project offers a new format for data science education at ETH involving collaborative learning between student bodies with different backgrounds and holographic reality technology. The goal is to improve learning strategies in order to
- better understand data science methods, including machine learning modeling and decision-making, and
- analyze & evaluate results with an eye on the trade-off between model performance and interpretability through a use case-based interactive team experience with 3D immersive technology, i.e. holographic reality.
The project is in collaboration with the Chair of Management Information Systems (MIS) at the ETHZ department of Management, Technology, and Economics (MTEC).
Results
The results show that our collaborative immersive analytics (CIA) system can elicit sustained collaboration among users with different backgrounds. Furthermore, we provide recommendations for the design of CIA systems that enable interdisciplinary teams to jointly solve ML tasks:
Takeaway 1: Pair analytics can be an effective method to elicit collaboration for ML
tasks in co-located, synchronous immersive settings, as it is intuitive and allows users to
share context and visualizations effectively.
Takeaway 2: Clear role division promotes effectiveness in solving ML tasks.
Takeaway 3: Designers of CIA systems should explore ways to encourage critical thinking
in order to avoid overconfidence in ML models.
Takeaway 4: The use of multiple types of interfaces (2D and 3D) does not impede
collaborative efficiency when solving ML tasks.
Takeaway 5: Handovers, although decreasing efficiency, are a driver of collaboration in
co-located, synchronous IA; designs of CIA systems for ML should anticipate their use.
Takeaway 6: Designing CIA systems should consider mechanisms to (re-)establish common
ground, especially for complex ML visualization (e.g., through affordances).
Takeaway 7: Consensus in collaboration and effectiveness in ML modeling within CIA
systems is driven by prior ML knowledge, thus highlighting the need for ML training
among both user pairs.
Takeaway 8: The use of several different interfaces (2D and 3D) does not reduce users’
levels of engagement in solving ML tasks, especially for users who are more knowledgeable
in ML, and may help avoid mental and physical overload.
Relevant publications:
Ferrario, A., Weibel, R., & Feuerriegel, S. (2020, April). ALEEDSA: Augmented reality for interactive machine learning. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-8).
Benk, M., Weibel, R. P., Feuerriegel, S., & Ferrario, A. (2022). ” Is It My Turn?” Assessing Teamwork and Taskwork in Collaborative Immersive Analytics. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1-23
Lead Researchers
Dr. Raphaël Weibel, Michaela Benk
Project Status
Completed
