Building the most creative and innovative collaborative groups using bayes classifiers

Abstract

Building “the best” creative and innovative groups that have common goals and tasks to perform, efficiently and effectively, is difficult. The complexity of this undertaking is significantly increased by the necessity to first understand and then measure what “the best” goal means for the individuals in the groups, but also for each group as a whole. We present here our Bayes classifiers-based technique for building “the best” groups of students to work together in collaborative learning situations. In our case, “the best” goal means the most creative and innovative teams possible in a given learning situation based on some particular attributes: individual creativity, motivation, domain knowledge, and inter-personal affinities. However, both the proposed model and method are general and they may be used for building collaborative groups in any situation, with the appropriate “the best” goal and attributes. A case study on using this method with our Computer Science students is also included.