Towards Team Formation Using Belbin Role Types and a Social Networks Analysis Approach

Flores-Parra, Josue-Miguel; Castafon-Puga, Manuel; Rosales-Cisneros, Ricardo; Gaxiola–Pacheco, Carelia

doi:10.1109/temscon.2018.8488386

Cited by 18 publications

(9 citation statements)

References 28 publications

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“…addition, it allows expanding other works like [45] about the application of affinity sociograms for forming self-organized collaborative work groups.…”

Section: F Discussionmentioning

confidence: 99%

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Relationships Between Social Interactions and Belbin Role Types in Collaborative Agile Teams

2023

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Incorporating technologies to support the monitoring, evaluation, and feedback of soft skills attracts great attention in the scientific community. For a team to have a good performance and manage itself, it can be helpful to identify the behavioral characteristics and social interactions established between its members. Here, we explore the relationships between Belbin's behavioral roles, affinity sociograms, and audio interactions collected by the Naira multimodal analysis platform. We present a case study with university students designing with the Lego Serious Play methodology and incorporating agile practices that would stimulate collaborative work. The results are promising, allowing us to define new research hypotheses that will serve as guidelines for future work. The findings indicate that the Naira platform would enable us to recognize behavioral traces for the Social and Action natural roles.

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“…addition, it allows expanding other works like [45] about the application of affinity sociograms for forming self-organized collaborative work groups.…”

Section: F Discussionmentioning

confidence: 99%

“…In [45], an approach closer to the present work is presented. The authors use Belbin on a group of Computer Engineering undergraduate students to define the dominant VOLUME 4, 2016 roles and profiles of the participants.…”

Section: Related Workmentioning

confidence: 99%

Relationships Between Social Interactions and Belbin Role Types in Collaborative Agile Teams

2023

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“…Its structure is open through decentralized ecosystems of interaction where actors play a relevant role as content creators where they not only consume content but generate and share it for educational purposes. Through these processes each individual has the opportunity to argue on a topic of their interest [25], which occurs in a group or in virtual communities of common interests [3,26]. These characteristics suggest that social networks can be considered as an educational medium [27][28][29].…”

Section: Social Network and Their Relation With Informal Learningmentioning

confidence: 99%

Informal Learning in Social Networks During the COVID-19 Pandemic: An Empirical Analysis

Ocaña

Mejía

Larrea

et al. 2021

Artificial Intelligence, Computer and Software Engineering Advances

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The use of the Internet and social networks have increased dramatically during the COVID quarantine mainly because several activities were moved online. In education, numerous stakeholders stayed at home and their academic plans were modified and adapted to an entire virtual environment. This was the case of a live event (Science Café) whose purpose was to disseminate knowledge through Facebook and YouTube. Thus, this study aimed at verifying if there was knowledge construction in social networks through user interactions by using 1,083 comments posted by the audience. Comments were coded according to validated frameworks for language taxonomy and collaborative knowledge construction. Results show that the predominant interaction is that in which viewers pose questions to speakers. Our analyses also revealed that attendees hardly reached the highest levels of knowledge construction through unguided interaction. Often, user interactions went beyond emotional expressions towards evaluation and therefore, could reach a higher level of knowledge construction. This study shows that social networks may offer informal spaces for deliberation and collaborative interaction with the potential to support learning if guided properly. This research aims to contribute empirical evidence to the growing body of literature that online interactions in informal environments may provide productive learning.

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“…These approaches have focused only on the expertise of recruited team members and did not consider the interaction within members. Other approaches limited their focus on team formation in SNs and proposed a solution to hire teams with good social relationships indifferent of the members' levels of expertise [36]- [38]. The authors in [39] have shed the light on the CMCS paradigm by providing a detailed CMCS taxonomy and investigating the challenges in designing CMC tasks.…”

Section: Related Workmentioning

confidence: 99%

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Hamrouni¹,

Ghazzai²,

Alelyani³

et al. 2021

Preprint

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Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this paper, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: i) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and ii) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its Social Network (SN) neighbors. We first formulate the recruitment as an Integer Linear Program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: level of expertise, social relationship strength, recruitment cost, and recruiter's confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on Graph Neural Networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers' search space and afterwards, exploiting a meta-heuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world dataset illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.

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Towards Team Formation Using Belbin Role Types and a Social Networks Analysis Approach

Cited by 18 publications

References 28 publications

Relationships Between Social Interactions and Belbin Role Types in Collaborative Agile Teams

Relationships Between Social Interactions and Belbin Role Types in Collaborative Agile Teams

Informal Learning in Social Networks During the COVID-19 Pandemic: An Empirical Analysis

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

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