Supporting Collaborative Reflection at Work: A Socio-Technical Analysis

This study presents an analysis of a tool that supports collaborative reflection at work. So far, research has focused on individual reflection or reflection in an educational context. Therefore, little is known about designing support for collaborative reflection at work. In four studies that use an application for collaborative reflection support, built based on prior empirical work, the paper presents an analysis of the ways workers used the tool for collaborative reflection. The analysis was based on log data and material from interviews and observations. The results show that there were different ways in which people used the application and that the impact of using it differed among groups. Besides positive effects, open issues in reflection support emerged. The paper presents insights on and design challenges for collaborative reflection support and potential solutions for these challenges. The findings are related to a model of collaborative reflection support and they emphasize that such support needs to be understood as socio-technical in nature if it is to succeed in practice. Finally, the study proposes designs for further work on tools supporting collaborative reflection

Special Issue on: Awareness and Reflection in Technology Enhanced Learning. Vol.9, (2-3)

Awareness and reflection play a crucial role in the learning process, helping the involved actors to succeed in self-regulated learning and to optimise their learning experience. Whether in traditional education, workplace training or lifelong learning, appropriate feedback together with proper assessment of previous practices can bring benefits for all the participants and cultivate their reflective skills, which are essential for effective learning

How to Interact with Augmented Reality Head Mounted Devices in Care Work? A Study Comparing Handheld Touch (Hands-on) and Gesture (Hands-free) Interaction

In this paper, we investigate augmented reality (AR) to support caregivers. We implemented a system called Care Lenses that supported various care tasks on AR head-mounted devices. For its application, one question concerned how caregivers could interact with the system while providing care (i.e., while using one or both hands for care tasks). Therefore, we compared two mechanisms to interact with the Care Lenses (handheld touch similar to touchpads and touchscreens and head gestures). We found that head gestures were difficult to apply in practice, but except for that the head gesture support was as usable and useful as handheld touch interaction, although the study participants were much more familiar with the handheld touch control. We conclude that head gestures can be a good means to enable AR support in care, and we provide design considerations to make them more applicable in practice

Trust dynamics and verbal assurances in human robot physical collaboration

Trust is the foundation of successful human collaboration. This has also been found to be true for human-robot collaboration, where trust has also influence on over- and under-reliance issues. Correspondingly, the study of trust in robots is usually concerned with the detection of the current level of the human collaborator trust, aiming at keeping it within certain limits to avoid undesired consequences, which is known as trust calibration. However, while there is intensive research on human-robot trust, there is a lack of knowledge about the factors that affect it in synchronous and co-located teamwork. Particularly, there is hardly any knowledge about how these factors impact the dynamics of trust during the collaboration. These factors along with trust evolvement characteristics are prerequisites for a computational model that allows robots to adapt their behavior dynamically based on the current human trust level, which in turn is needed to enable a dynamic and spontaneous cooperation. To address this, we conducted a two-phase lab experiment in a mixed-reality environment, in which thirty-two participants collaborated with a virtual CoBot on disassembling traction batteries in a recycling context. In the first phase, we explored the (dynamics of) relevant trust factors during physical human-robot collaboration. In the second phase, we investigated the impact of robot’s reliability and feedback on human trust in robots. Results manifest stronger trust dynamics while dissipating than while accumulating and highlight different relevant factors as more interactions occur. Besides, the factors that show relevance as trust accumulates differ from those appear as trust dissipates. We detected four factors while trust accumulates (perceived reliability, perceived dependability, perceived predictability, and faith) which do not appear while it dissipates. This points to an interesting conclusion that depending on the stage of the collaboration and the direction of trust evolvement, different factors might shape trust. Further, the robot’s feedback accuracy has a conditional effect on trust depending on the robot’s reliability level. It preserves human trust when a failure is expected but does not affect it when the robot works reliably. This provides a hint to designers on when assurances are necessary and when they are redundant

HOW TO INTERACT WITH AR HEAD MOUNTED DEVICES IN CARE WORK? A STUDY COMPARING HANDHELD TOUCH (HANDS-ON) AND GESTURE (HANDS-FREE) INTERACTION

In this paper, we describe a study investigating augmented reality (AR) to support caregivers. We implemented a system called Care Lenses that supports various care tasks on AR head-mounted devices. For its application, one question was how caregivers could interact with the system while providing care, that is, while using one or both hands for care tasks. Therefore, we compared two mechanisms to interact with the CareLenses (handheld touch similar to touchpads and touchscreens and head gestures). We found that certain head gestures were difficult to apply in practice, but that except from this head gesture support was as usable and useful as handheld touch interaction, although the study participants were much more familiar with the handheld touch control. We conclude that head gestures can be a good means to enable AR support in care, and we provide design considerations to make them more applicable in practice

GROUP 2018 Special Issue Guest Editorial: Another 25 Years of GROUP

For over 25 years, the ACM International Conference on Supporting GroupWork (GROUP) has been and will continue to be the premier venue for research on Computer-Supported Cooperative Work,Human–Computer Interaction, Computer-Supported Collaborative Learning, and Socio-Technical Studies. The three papers in this special issue demonstrate GROUP’s continued commitment to diverse research approaches, emerging technologies, and collaborative work. We hope you enjoy these papers and, like us, look forward to another 25 years of GROUP.https://deepblue.lib.umich.edu/bitstream/2027.42/146739/1/Robert et al. 2018.pdfDescription of Robert et al. 2018.pdf : Articl

Excuse Me, Something Is Unfair! - Implications of Perceived Fairness of Service Robots

Fairness is an important aspect for individuals and teams. This also applies for human-robot interaction (HRI). Especially if intelligent robots provide services to multiple humans, humans may feel treated unfairly by robots. Most work in this area deals with the aspects of fair algorithms, task allocation and decision support. This work focuses on a different, yet little explored perspective, which looks at fairness in HRI from a human-centered perspective in human-robot teams. We present an experiment in which a service robot was responsible for distributing resources among competing team members. We investigated how different strategies of distribution influence the perceived fairness and the perception of the robot. Our study shows that humans might perceive technically efficient algorithms as unfair, especially if humans personally experience negative consequences. This also had negative impact on human perception of the robot, which should be considered in the design of future robots