Research on Multifeature-Based Superposter Identification in Online Learning Forums

Abstract

With the development of online learning and distance education, online learners' discussions in forums become increasingly effective to facilitate learning. Superposters, who play a more and more important role in forums, have attracted researchers' close attention. The key to the research is how to identify superposters among a large number of participants. Some studies focus on the network interaction of superposters and some content-related features but neglect the basic quality like language expression that a superposter should possess and the learning-related features like learning collaboration. Based on the analysis of online learning corpus, through network interaction and combination of the different features of N-gram, the paper proposed the superposter identification method based on the three primary features including language expression (L), content quality (C), and social network interaction (S) and the eight secondary features including learning collaboration. The paper applied the method in the real online learning forum corpus for identifying 28 preset superposters, achieving the results of P@15=1.0, Avg.P@15=1.0, P@28=0.86, and Avg.P@28=0.95. Experiments showed that this was an effective superposter identification method in online learning forums.