Building Textual Fuzzy Interpretive Structural Modeling to Analyze Factors of Student Mobility

Abstract

There is a large amount of textual data present on web content that has the potential to answers many open questions in the field of humanities and human behavior. We have developed a novel methodology, called Textual Fuzzy Interpretive Structural Modeling (TFISM), that automatically analyses large textual datasets to identify the internal and external relationships between factors in student mobility. This methodology enhances approaches of Interpretive Structural Modeling (ISM) to allow the input type to be textual data. It is multi-disciplinary and integrates ISM with techniques from Artificial Intelligence, Text extraction, and information retrieval. We have validated this methodology on two different datasets from social media and academic articles. In this paper, we present the results of our study to identify the critical factors and most effective factors for global student mobility.