Augmenting Collaboration with Invisible Data

Makhkamova, Alina; Ziegler, Pascal; Werth, Dirk

doi:10.1145/3340764.3344908

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…Research has shown that providing affective feedback augments affective awareness, which facilitates emotion control and behavioral self‐regulation, thereby ameliorating performance in both individuals and teams. In teams specifically, affective feedback has been shown to enhance social interactions, collaboration, learning, and eventually collaborative performance, because it helps team members to understand one another better (Eligio, Ainsworth, & Crook, 2012; Järvenoja & Järvelä, 2009; Makhkamova, Ziegler, & Werth, 2019). Moreover, potential benefits of increased emotional awareness at the group level include improved communication, increased trust, and group cohesion (Saari, Kallinen, Salminen, Ravaja, & Yanev, 2008).…”

Section: Adaptive Feedback Systems To Augment Team Interactionsmentioning

confidence: 99%

“…Likewise, there are multiple ways of providing affective feedback besides the HeartBees application already mentioned (Qin, Choi, Constantinides, Aiello, & Quercia, 2020; Qin, Constantinides, et al., 2020). Affective feedback can be provided both at the individual level and at a group level based on aggregated individual measures (e.g., Makhkamova et al., 2019). In a study by Feidakis, Daradoumis, Caballé, and Conesa (2014), for example, implicit affective feedback was given to individuals based on self‐reported affective states by a virtual agent providing hints or jokes in order to help with task completion or tension release.…”

Section: Adaptive Feedback Systems To Augment Team Interactionsmentioning

confidence: 99%

“…However, this type of implicit feedback increases cognitive load during interaction and might prove to be distracting. Therefore, Makhkamova et al (2019) introduced a more abstract way of providing affective feedback using shapes and colors, following guidelines by Bartram, Patra, and Stone (2017). For instance, when a subject was anxious, the shape around their avatar would be smoother and blue, whereas when the team state was recognized as happy and excited, the icon representing team affective state was yellow and irregular.…”

Section: Affective Feedbackmentioning

confidence: 99%

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Prospects for Augmenting Team Interactions with Real‐Time Coordination‐Based Measures in Human‐Autonomy Teams

Wiltshire

Eijndhoven

Halgas

et al. 2022

Topics in Cognitive Science

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Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human‐autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams’ coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human‐autonomy teams that augment team interactions using coordination‐based measures.

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Section: Adaptive Feedback Systems To Augment Team Interactionsmentioning

confidence: 99%

Section: Adaptive Feedback Systems To Augment Team Interactionsmentioning

confidence: 99%

Section: Affective Feedbackmentioning

confidence: 99%

See 1 more Smart Citation

Prospects for Augmenting Team Interactions with Real‐Time Coordination‐Based Measures in Human‐Autonomy Teams

Wiltshire

Eijndhoven

Halgas

et al. 2022

Topics in Cognitive Science

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“…Such a claim has received in the last decade an increasing interest in the affective, computer, and learning sciences from different theoretical, technical, methodological and empirical standpoints (ibid.). Applications of an EAT in computer-mediated learning environments may in fact range from providing learners with the possibility to self-report their emotions during a distance learning course as a means to increase self-reflection and self-regulation (Lavoué, Molinari, & Trannoi, 2017) to the implementation of a multi-modal system that provide emotional awareness through an unobtrusive brain-computer interface during computer-mediated collaboration (Makhkamova, Ziegler, & Werth, 2019). Despite the differences in application, though, research gravitating around an EAT usually aims at investigating which factors -intrinsic to the tool, deriving from the interaction between learners and the tool, between learners themselves, as well as between learners and the instructional design -determine whether and how emotional awareness may be beneficial in computer-mediated learning environments.…”

Section: Introductionmentioning

confidence: 99%

Implementation and Assessment of a Multipurpose Appraisal-Driven Emotion Awareness Tool Based on Self-Report

Fritz

2023

Preprint

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In computer-mediated learning environments, especially in remote conditions, learners often lack the socio-affective cues that are usually available in face-to-face interactions. Socio-affective phenomena are known to play a prominent role at various levels of learning processes and outcomes, spanning high-order cognitive functions, motivation, sense of belonging, and quality of interaction with colleagues. Emotions, in particular, are nowadays considered dynamic and multifaceted phenomena that serve a wide range of adaptive functions. As a result, an interdisciplinary interest has recently emerged on ways by which computer-mediated learning environments may be endowed with emotional awareness, that is, information about one’s own emotions and/or the emotions of colleagues, which is instrumental to the learning task at hand. Emotion Awareness Tools are one such attempt to bestow learners with the possibility to produce and peruse emotional awareness through a dedicated interface that coexists with the overall learning environment. The thesis provides the details of the implementation and empirical assessment of an Emotion Awareness Tool with the following main characteristics. First, it is based on voluntary self-report of emotion. Second, it implements a computational structure of emotion rooted in appraisal theories of emotion, for which emotion elicitation and differentiation is a dynamic and ongoing process driven by a cognitive evaluation of the situation. Third, learners can produce and peruse emotional awareness through the tool on a moment-to-moment basis. Adopting an iterative design process, the implementation and assessment of the tool are guided by evidence gathered through empirical contributions aimed at investigating which factors – intrinsic to the tool, deriving from the interaction between learners and the tool, between learners themselves, as well as between learners and the instructional design – determine whether and how emotional awareness may be beneficial in computer-mediated learning environments. The main outcomes of the thesis are a toolbox that allows researchers and practitioners to configure an instance of the tool according to their own scientific or instructional goals, a structural causal model of the influence of emotional awareness on learning in computer-mediated environments, and methodological techniques or instruments that may be applied in similar contexts.

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Researches advanced in human-computer collaboration and human-machine cooperation: from variances to common prospect

Zhang

2022

2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)

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The booming technology of artificial intelligence (AI) and robotics enable people not only prospect its huge potential, but also limitation of capability when solving inplicit problems comparing with human. As a result, fields like Human- Computer Collaboration (HCC) and Human-Machine Cooperation (HMC) are developed and explored, with relevant concepts such as Human-in-The-Loop (HiTL) being aroused. Whereas, such types of research areas are lack of uniform standards and procedures, even measurements, attributing to the way too specific works to scenarios. Thus in this paper, a review of Human-Computer Collaboration (HCC) and Human-Machine Cooperation (HMC) is conducted from the aspects of vaiances to their common prospects. From data gathering to decision making and support, finally to interaction interface, the processing and operation of HCC/HMC systems are discussed, with key issues like trust level to agents proposed and future works suggested.

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Augmenting Collaboration with Invisible Data

Cited by 3 publications

References 18 publications

Prospects for Augmenting Team Interactions with Real‐Time Coordination‐Based Measures in Human‐Autonomy Teams

Prospects for Augmenting Team Interactions with Real‐Time Coordination‐Based Measures in Human‐Autonomy Teams

Implementation and Assessment of a Multipurpose Appraisal-Driven Emotion Awareness Tool Based on Self-Report

Researches advanced in human-computer collaboration and human-machine cooperation: from variances to common prospect

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