Adaptive group formation to promote desired behaviours

BACKGROUND There is substantial literature that shows the benefits of collaborative work, though these benefits vary enormously with circumstances. Irrespective of their structure and composition, groups usually exist for a particular reason and implicitly or explicitly target one or more outcomes. The achievements of group outcomes depend on many factors, including the individual behaviour of each group member. These behaviours are, in turn, affected by the individual characteristics, the context and the group composition. Constructing groups in a way that maximises the achievement of a specific outcome is complex with the optimal group composition depending on the attributes of the group members. Previous work has in most cases considered group formation based on one particular attribute, such as learning style, gender, personality, etc. Less common are instances of group formation rules being adjusted systematically to accommodate changes in an individualâs attributes or disposition. PURPOSE This paper considers how the multi-factorial nature of group performance and the variations in desired behaviour across different circumstances can be addressed within a consistent framework. DESIGN/METHOD The methodology consisted of two main stages. In the first stage, a simulation was encoded in MatLab to assess the conceptual approach of progressively updating rules for group formation. The method uses an unsupervised learning algorithm and correlation factors between quantifiable group characteristics (average age, degree of motivation, etc.) and resultant behaviours of the groups that are actually formed (level of dialogue, interface interactions, etc.) to update the rules used for group formation, and hence progressively construct groups that are more likely to behave in desired ways. The second stage involved an evaluation of this approach in a real world scenario using remotely accessible laboratories where engineering students voluntarily participated in a study in April 2012. RESULTS The simulation results show that under certain conditions the desired behaviour chosen with the intention of improving specific learning outcomes can be optimized and that groups can be constructed that are more likely to exhibit desired behaviour. The paper also reports preliminary evidence that shows the feasibility of this approach in selecting group participants in an engineering class to promote a desired outcome in this case independent learning. CONCLUSIONS This study demonstrates the feasibility of using a set of individual characteristics of group members to form groups that are more likely to have desired group behaviours and that these characteristics can be monitored and updated to dynamically alter group formation to account for changes in any individualâs characteristics. This has potential to allow groups formation decisions to be made dynamically to achieve a desired outcome, for example promote collaborative learning

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature- inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure