Analysis of Small Groups

Gática-Pérez, Daniel; Aran, Oya; Kumar, Shubham

doi:10.1017/9781316676202.025

Cited by 11 publications

(3 citation statements)

References 105 publications

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“…From a computational perspective, in recent years there has been a significant amount of work aimed at developing methods that automatically analyze group interaction, and models able to predict information about the participants as well as the state of the interaction, based on both spoken words and nonverbal channels. Related work reports on identification of group dynamics [1][2][3], group performance [4][5][6] and participation styles [7], and inferring behavioral aspects in dialogue such as competitiveness [8,9], dominance and leadership [10][11][12], affect [13], cohesion [14] and personality [15]. The aforementioned works focus mainly on nonverbal and turn-taking cues, while a smaller amount includes the investigation of verbal cues, reporting the usefulness of linguistic features [4] and linguistic alignment (repetitions) [16] for predicting group performance.…”

Section: Introductionmentioning

confidence: 99%

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Observing Collaboration in Small-Group Interaction

Koutsombogera

Vogel

2019

MTI

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In this study, we define and test measures that capture aspects of collaboration in interaction within groups of three participants performing a task. The measures are constructed upon turn-taking and lexical features from a corpus of triadic task-based interactions, as well as upon demographic features, and personality, dominance, and satisfaction assessments related to the corpus participants. Those quantities were tested for significant effects and correlations they have with each other. The findings indicate that determinants of collaboration are located in measures quantifying the differences among dialogue participants in conversational mechanisms employed, such as number and frequency of words contributed, lexical repetitions, conversational dominance, and in psychological and sentiment variables, i.e., the participants’ personality traits and expression of satisfaction.

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Section: Introductionmentioning

confidence: 99%

“…Small-group interactions are considered pervasive and quintessential of collaborative work [3]. A crucial goal within task-based interactions is to establish cooperation, since task-oriented small groups are most effective when all group members can participate and be heard [21].…”

Section: Introductionmentioning

confidence: 99%

Observing Collaboration in Small-Group Interaction

Koutsombogera

Vogel

2019

MTI

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“…The latter approach, ML techniques, identifies optimal parameters to capture patterns and regularities in a given system, such as are needed in risk assessment and document classification [21]. In small group research, both have been proposed as novel techniques to address relationship management in group conversations in order to understand group characteristics, make predictions, or both [22] (for a detailed review, see Gatica-Perez [23]).…”

Section: Introductionmentioning

confidence: 99%

Understanding health management and safety decisions using signal processing and machine learning

Aufegger

Bicknell

Soane

et al. 2019

BMC Med Res Methodol

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Background Small group research in healthcare is important because it deals with interaction and decision-making processes that can help to identify and improve safer patient treatment and care. However, the number of studies is limited due to time- and resource-intensive data processing. The aim of this study was to examine the feasibility of using signal processing and machine learning techniques to understand teamwork and behaviour related to healthcare management and patient safety, and to contribute to literature and research of teamwork in healthcare. Methods Clinical and non-clinical healthcare professionals organised into 28 teams took part in a video- and audio-recorded role-play exercise that represented a fictional healthcare system, and included the opportunity to discuss and improve healthcare management and patient safety. Group interactions were analysed using the recurrence quantification analysis (RQA; Knight et al., 2016), a signal processing method that examines stability, determinism, and complexity of group interactions. Data were benchmarked against self-reported quality of team participation and social support. Transcripts of group conversations were explored using the topic modelling approach (Blei et al., 2003), a machine learning method that helps to identify emerging themes within large corpora of qualitative data. Results Groups exhibited stable group interactions that were positively correlated with perceived social support, and negatively correlated with predictive behaviour. Data processing of the qualitative data revealed conversations focused on: (1) the management of patient incidents; (2) the responsibilities among team members; (3) the importance of a good internal team environment; and (4) the hospital culture. Conclusions This study has shed new light on small group research using signal processing and machine learning methods. Future studies are encouraged to use these methods in the healthcare context, and to conduct further research on how the nature of group interaction and communication processes contribute to the quality of team and task decision-making. Electronic supplementary material The online version of this article (10.1186/s12874-019-0756-2) contains supplementary material, which is available to authorized users.

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Speech Pause Patterns in Collaborative Dialogs

Koutsombogera

Vogel

2019

Intelligent Systems Reference Library

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This chapter discusses the multimodal analysis of human behavioral data from the big data perspective. Though multimodal big data bring tremendous opportunities for related applications, we present current challenges in the domain of multimodal and behavioral analytics. We argue that in the case of analysing human behavior in interaction, we need to shift to the analysis of samples, smaller datasets, before scaling up to large data collections. We describe a dataset developed to study group collaborative interaction from measurable behavioral variables. As a case study, we investigate speech pauses and their patterns in the data, as well as their relationship to the topics of the dialog and the turn-taking mechanism, and we discuss their role in understanding the structure of collaborative interactions as well as in interpreting the behavior of the dialog participants.

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Analysis of Small Groups

Cited by 11 publications

References 105 publications

Observing Collaboration in Small-Group Interaction

Observing Collaboration in Small-Group Interaction

Understanding health management and safety decisions using signal processing and machine learning

Speech Pause Patterns in Collaborative Dialogs

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