Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Expert Systems with Applications: An International Journal
Adaptation patterns in systems for scripted collaboration
CSCL'09 Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1
Applying the genetic encoded conceptual graph to grouping learning
Expert Systems with Applications: An International Journal
Forming reasonably optimal groups: (FROG)
Proceedings of the 16th ACM international conference on Supporting group work
Improving Group Selection and Assessment in an Asynchronous Collaborative Writing Application
International Journal of Artificial Intelligence in Education
Forming project groups while learning about matching and network flows in algorithms
Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education
Intelligent Decision Technologies - Special issue on Multimedia/Multimodal Human-Computer Interaction in Knowledge-based Environments
ADNTIIC'11 Proceedings of the Second international conference on Advances in New Technologies, Interactive Interfaces and Communicability
Creating effective student groups: an introduction to groupformation.org
Proceeding of the 44th ACM technical symposium on Computer science education
Team knowledge with motivation in a successful MMORPG game team: A case study
Computers & Education
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Teachers interested in small-group learning can benefit from using psychological factors to create heterogeneous groups. In this paper we describe a computer-supported grouping system named DIANA that uses genetic algorithms to achieve fairness, equity, flexibility, and easy implementation. Grouping was performed so as to avoid the creation of exceptionally weak groups. We tested DIANA with 66 undergraduate computer science students assigned to groups of three either randomly (10 groups) or using an algorithm reflecting [Sternberg, R. J. (1994). Thinking styles: theory and assessment at the interface between intelligence and personality. In R. J. Sterberg, & P. Ruzgis (Eds.), Personality and Intelligence (pp. 169-187). New York: Cambridge University Press.] three thinking styles (12 groups). The results indicate that: (a) the algorithm-determined groups were more capable of completing whatever they were ''required to do'' at a statistically significant level, (b) both groups were equally capable of solving approximately 80% of what they ''chose to do,'' and (c) the algorithm-determined groups had smaller inter-group variation in performance. Levels of satisfaction with fellow group member attitudes, the cooperative process, and group outcomes were also higher among members of the algorithm-determined groups. Suggestions for applying computer-supported group composition systems are offered.