Enhancing the automation of forming groups for education with semantics

Many approaches to learning and teaching rely upon students working in groups. Formation of optimal groups can be a time consuming and complex task, particularly when the list of participants is unknown in advance. This research investigates the implementation of semantics to enhance computer-supported group formation in education using two approaches: The first approach uses semantics to express the criteria specified by the person forming the groups. The group formation in this approach is modelled as a constraint satisfaction problem where the criteria is a set of constraints that we aim to minimise their violation while processing the groups. The second approach uses Semantic Web domain ontologies in describing the participants to enrich the data used in calculating the similarity between the participants when the group formation is processed using a heuristic approach such as clustering algorithms. We run a number of experiments that include real datasets from higher education classes, simulated datasets, Web-based datasets, and user studies, to evaluate the research. The results proved that in both approaches, implementing semantics improved the generated groups, in that, using semantics to model group formation’s constraints generates an optimised grouping in terms of constraint satisfaction that exceeds the performance of existing applications, particularly in terms of the number of constraints it can handle; and that using semantics to model the participants’ data enhances their satisfaction with the groups they are allocated to.