Belbin's Team Role Model: Development, Validity and Applications for Team Building*

Aritzeta, Aitor; Swailes, Stephen; Senior, Barbara

doi:10.1111/j.1467-6486.2007.00666.x

Cited by 97 publications

(78 citation statements)

References 45 publications

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“…For example, in (Costaguta et al, 2012) the authors present a multi-agent model that monitors students' participation in a group and proposes corrective actions when the group behavior is far from the ideal balance of team roles. In (Aritzeta et al 2007) the authors reaffirmed the theory about balanced teams performing better over the long run than imbalanced ones. Similarly, in (Winter 2004) it is proposed a group model that combines Belbin's team roles with team members' knowledge, type of task that needs to be done, and group contextual factor (such as localization and time pressures).…”

Section: Observed Interactionssupporting

confidence: 56%

Redes Bayesianas para detección de roles de equipos en aprendizaje colaborativo soportado por computadoras

2015

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Resumen: El trabajo colaborativo soportado por computadoras permite a los estudiantes que se encuentran en lugares remotos trabajar de manera conjunta en el mismo entorno virtual y permite la comunicación de ideas e información entre los integrantes del grupo. Sin embargo, como no todos los estudiantes son iguales, es importante estudiar las características de éstos para construir grupos de trabajo más productivos. La teoría de roles de equipo posibilita obtener buen desempeño en los equipos de trabajo considerando habilidades individuales, combinando las falencias de cada rol con las fortalezas de los otros. Generalmente, las personas tienen que completar extensos cuestionarios para poder determinar sus roles de equipo. En este trabajo, se propone un método alternativo para realizar esta detección a través de un sistema de aprendizaje colaborativo y a partir de la utilización de la técnica de Redes Bayesianas. Palabras clave: roles de equipo; trabajo colaborativo soportado por computadoras; redes Bayesianas.Abstract: Computer-supported collaborative learning allows students who are in different places to work together in the same virtual space, and supports the communication of ideas and information among learners. However, as not all students are identical, it is important to study users' characteristics to build more productive teams. Team Roles Theory allows obtaining very good team performance taking into account individual skills, combining the weaknesses of each role with the strengths of others. Originally, people have to complete extensive questionnaires to determine their team role. In this work we propose an alternative method to make this detection through a collaborative learning system and by using a Bayesian Network.

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Section: Observed Interactionssupporting

confidence: 56%

Redes Bayesianas para detección de roles de equipos en aprendizaje colaborativo soportado por computadoras

2015

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“…Para modelar los estilos de aprendizaje utilizamos el modelo formulado por Felder y Silverman debido a que ha sido evaluado exitosamente en diferentes dominios [Felder and Brent 2005]. De forma similar, para modelar los roles de equipo utilizamos la teoría propuesta por Meredith Belbin, reconocida como una de las teorías de roles de equipos más difundida y validada [Aritzeta et al 2007]. …”

Section: Introductionunclassified

Influencia de estilos de aprendizaje y roles de equipo en el aprendizaje colaborativo soportado por computadoras

Nicoletti¹,

Balmaceda²,

Schiaffino³

et al. 2013

Simpósio Brasileiro De Informática Na Educação

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Abstract. A key aspect when students have to work in groups in a computer supported collaborative learning context is how the groups are formed. In this article, we analyze how two types of students' characteristics might influence on the success or the failure of the collaborative learning process when students have to work in groups. Particularly, we study how students' learning styles and students' team roles might affect the performance of the team. By applying Artificial Intelligence techniques we have discovered interesting relationships among these students' characteristics and team performance. We consider that these findings can shed some light to the area of group formation in the area of computer supported collaborative learning.

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“…According to Belbin, team roles are defined as a pattern of six factors: personality, mental ability, current values and motivation, field constraints, experience, and role learning. Although Belbin (2010) did not show how much of the variance of a specific role is explained by individual factors, he argued that all roles should acquire a balanced representation in a team (Aritzeta, Swailes, & Senior, 2007). However, not all studies could verify the Belbin roles' contribution to innovation and performance (Anderson & Spleap, 2004;Rushmer, 1996).…”

Section: Introductionmentioning

confidence: 99%

Effects of Team Structure on Innovation Performance: An Empirical Study

2013

CBR

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Teamwork is gaining increasing attention in a broader management research. In addition to previous research on the relationship between team structure and innovation performance, this study draws from an interesting experience-based theory advanced by Kelley and Littman (2005), which examines teams from design thinking perspective, and tests its contributions and effects on team's innovation performance. According to Kelley and Littman any team should include the following team roles: The anthropologist, the experimenter, the cross-pollinator, the hurdler, the collaborator, the director, the experience architect, the set designer, the storyteller, and the caregiver. We develop theoretical logics to explain how team structure that includes these key team roles and competences lead to a better innovation performance, and propose pertinent hypotheses.Experimental-empirical research and quantitative analysis were used in the study. The study conducted multiple experiments on three samples: a group of foreign entrepreneurship students, a group of technical students, and an additional group of randomly selected individuals, aged between 20 and 58, with diverse backgrounds. A special approach was implemented and a new instrument was developed to evaluate individuals in teams. While the results show that team that possess the major competences proposed by Kelley and Littman are more innovative, preliminary results also show that not all team roles are equally important. Moreover, team roles should be allocated equally among members for better collaboration, member satisfaction, and quick response, and within one team, one prevailing personality is optimal in terms of innovativeness. We discuss the implications of our findings for future research and managerial practice.

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Belbin's Team Role Model: Development, Validity and Applications for Team Building*

Cited by 97 publications

References 45 publications

Redes Bayesianas para detección de roles de equipos en aprendizaje colaborativo soportado por computadoras

Redes Bayesianas para detección de roles de equipos en aprendizaje colaborativo soportado por computadoras

Influencia de estilos de aprendizaje y roles de equipo en el aprendizaje colaborativo soportado por computadoras

Effects of Team Structure on Innovation Performance: An Empirical Study

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