Investigating eLearning Research Trends in Iran via Automatic Semantic Network Generation

The purpose of this study is to investigate Iran’s eLearning research status in comparison with the world. We propose a method based on a text mining approach for extracting knowledge from Iranian published articles and generating the corresponding semantic network automatically. eLearning concepts are extracted from papers published in 6 years’ proceedings of ICeLeT, an International Conference on eLearning and eTeaching, in Iran. After extracting the domain-specific concepts, each pair of concepts get the possibility to be linked together based on co-occurrence in the articles. A weight is assigned to each edge according to the pointwise mutual information value of the pair of concepts. To identify gaps between the latest local and global research, the obtained semantic network is compared with another semantic network extracted from 6 years’ proceedings of ICALT, an International Conference on Advanced Learning Technologies. By applying a hybrid clustering algorithm on two networks based on the combination of label propagation and Markov clustering, and identifying the differences between node memberships and hubs, strengths and weaknesses of each network are demonstrated.