Universal Optimization Framework: Leader-Centered Learning Team Formation Based on Fuzzy Evaluations of Learners and E-CARGO

Abstract

Building the right learning teams is a key to the success of collaborative learning in online and offline learning environments. However, existing research on learning team formation (LTF) ignores the uncertainty of learners’ abilities and lacks a common problem modeling and optimization approach. Aiming at the characteristics of two typical types of leader-centered (LC) LTF problems, a universal optimization framework of LC-LTF is proposed by introducing role-based collaboration (RBC) theory. This framework evaluates the comprehensive ability of learners via a fuzzy description mechanism; applies the environments–classes, agents, roles, groups, and objects (E-CARGO) model to formulate the LC-LTF problem; and employs an optimization platform to obtain an optimal solution. A case study demonstrates the effectiveness and feasibility of the proposed framework.