Influence Maximization for MOOC Learners Using BAT Optimization Algorithm

Abstract

The Ubiquitous behaviour of MOOCs for online learning has proven its importance specially in the Covid period. These platforms facilitate learners for peer support by communicating through the discussion forum. The communication held among learners is demonstrated through the social network (SN). The objective of this research is to analyse learner’s SN to find the seed of learners that maximizes the influence spread in the SN to handle its multi-objective research paradigm and avoid the influence maximization process of getting stuck in local optima. Henceforth, extensive experiments are performed using SN topological characteristics to build an effective objective function for the influence maximization problem, and BAT optimization algorithm is employed to achieve global optimum results to find out top influence spreader in course communication network. Efficient results have been obtained by the proposed approach which will help MOOC portals for substantial performance identification of influential learners as compared to ego-centric influential learner identification outcome.