A deterministic crowding evolutionary algorithm to form learning teams in a collaborative learning context

Yannibelli, Virginia; Amandi, Analía

doi:10.1016/j.eswa.2012.01.195

Cited by 60 publications

(57 citation statements)

References 22 publications

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“…Studies of multi-player games have consequently focused on understanding what motivates players' choices of teammates and the relationship with performance, with one key finding being that large variations in competence within teams discourages repeated interactions (Alhazmi et al 2017). This is in line with findings from collaborative learning environments, in which approaches that focus on automatically-forming optimally balanced student teams have been found to perform better than manual allocation to the student teams by experts (Yannibelli and Amandi 2012;Bergey and King 2014). Considering feedback from student collaborations has also been found to improve the group formation process (Srba and Bielikova 2015).…”

Section: Advantages and Challenges Of Collaborative Gamesmentioning

confidence: 79%

Reflective agents for personalisation in collaborative games

2018

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The collaborative aspect of games has been shown to potentially increase player performance and engagement over time. However, collaborating players need to perform well for the team as a whole to benefit and thus teams often end up performing no better than a strong player would have performed individually. Personalisation offers a means for improving overall performance and engagement, but in collaborative games, personalisation is seldom implemented, and when it is, it is overwhelmingly passive such that the player is not guided to goal states and the effectiveness of the personalisation is not evaluated and adapted accordingly. In this paper, we propose and apply the use of reflective agents to personalisation ('reflective personalisation') in collaborative gaming for individual players within collaborative teams via a combination of individual player and team profiling in order to improve player and thus team performance and engagement. The reflective agents self-evaluate, dynamically adapting their personalisation techniques to most effectively guide players towards specific goal states, match players and form teams. We incorporate this agent-based approach within a microservices architecture, which itself is a set of collaborating services, to facilitate a scalable and portable approach that enables both player and team profiles to persist across multiple games. An experiment involving 90 players over a two-month period was used to comparatively assess three versions of a collaborative game that implemented reflective, guided, and passive personalisation for individual players within teams. Our results suggest that the proposed reflective personalisation approach improves team player performance and engagement within collaborative games over guided or passive personalisation approaches, but that it is especially effective for improving engagement.

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Section: Advantages and Challenges Of Collaborative Gamesmentioning

confidence: 79%

Reflective agents for personalisation in collaborative games

2018

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“…In view of the difficulties of traditional genetic algorithm in the application in grouping problem, such as the increasing of the search space size, a hybrid grouping genetic algorithm, which obtains a compact algorithm, is introduced to solve the team formation problem based on group technology. Yannibelli and Amandi (2012) analyzed the learning team formation in a collaborative learning context by considering the knowledge of students, and achieved the objective of the balance of team. A deterministic crowing evolutionary, which is suitable to design different alternatives of the divisions of students and to evaluate the alternatives according to the grouping criterion, is introduced to divide the students into the given number of team.…”

Section: Related Literatures On Team Formationmentioning

confidence: 99%

“…The team members are selected according to the team member properties, and are matched to the appropriate team positions or roles. The aim of the selection process is to form a team successfully, and meanwhile to complete the task or achieve the objective of team (Dorn et al, 2011;Boon and Sierksma, 2003;Yannibelli and Amandi, 2012). In general, the selection of team members and the formation of a team are the judgments made by the team leader (Tavana et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A win–win team formation problem based on the negotiation

Wang

Jia

2015

Engineering Applications of Artificial Intelligence

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“…Despite the popularity of the Belbin model, there are only a few computationalbased approaches that focus on team formation based on this model. One of these approaches was proposed by Yannibelli and Amandi [29,94]. It is based on hybrid evolutionary algorithms to find the most suitable collaborative learning teams in the context of software engineering modules.…”

Section: Related Workmentioning

confidence: 99%

An artificial intelligence tool for heterogeneous team formation in the classroom

Alberola

Val

Sánchez-Anguix

et al. 2016

Knowledge-Based Systems

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Nowadays, there is increasing interest in the development of teamwork skills in the educational context. This growing interest is motivated by its pedagogical effectiveness and the fact that, in labour contexts, enterprises organize their employees in teams to carry out complex projects. Despite its crucial importance in the classroom and industry, there is a lack of support for the team formation process. Not only do many factors influence team performance, but the problem becomes exponentially costly if teams are to be optimized. In this article, we propose a tool whose aim it is to cover such a gap. It combines artificial intelligence techniques such as coalition structure generation, Bayesian learning, and Belbin's role theory to facilitate the generation of working groups in an educational context. This tool improves current state of the art proposals in three ways: i) it takes into account the feedback of other teammates in order to establish the most predominant role of a student instead of self-perception questionnaires; ii) it handles uncertainty with regard to each student's predominant team role; iii) it is iterative since it considers information from several interactions in order to improve the estimation of role assignments. We tested the performance of the proposed tool in an experiment involving students that took part in three different team activities. The experiments suggest that the proposed tool is able to improve different teamwork aspects such as team dynamics and student satisfaction.

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A deterministic crowding evolutionary algorithm to form learning teams in a collaborative learning context

Cited by 60 publications

References 22 publications

Reflective agents for personalisation in collaborative games

Reflective agents for personalisation in collaborative games

A win–win team formation problem based on the negotiation

An artificial intelligence tool for heterogeneous team formation in the classroom

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