Steiner Tree-based Collaborative Learning Group Formation in Trust Networks

Group formation is one of the key problems for collaborative learning, i.e., how to allocate agents (learners) to appropriate groups in order to improve the learning utility of the system. Previous works often focus on investigating the potential factors that may influence the agent's learning utility from the perspective of intrinsic attributes of agents; however, the structural attributes of groups are rarely considered. Considering that trust is an important interactive and cognitive attribute in collaborative learning, which can influence not only the incentive of learners collaborating in a group but also the promotion of skills of agents in knowledge sharing, this paper studies the collaborative learning group formation problem in trust networks. We propose a Steiner tree-based group formation algorithm, which first allocates appropriate agents to groups as initiators by considering the skill mastery and the strength of trust in the groups to guarantee the opportunities for skill promotion and then select followers by searching locally in the trust network. Through experiments based on real-world network datasets, we validate the performance of the proposed algorithm by comparing to several benchmarks, e.g., the graph partitioning-based group formation algorithm and the simulated annealing-based group formation algorithm.