Highlighting trend-setters in educational platforms by means of formal concept analysis and answer set programming

Abstract

Web-based educational systems offer unique opportunities to study how students learn and based on the analysis of the users’ behavior, to develop methods to improve the e-learning system. These opportunities are explored, in the current paper, by blending web usage mining techniques with polyadic formal concept analysis and answer set programming. In this research, we consider the problem of investigating browsing behavior by analyzing users’ behavioral patterns on a locally developed e-learning platform, called PULSE. Therefore, we investigate users’ behavior by using similarity measures on various sequences of accessed pages in a tetradic and a pentadic setting. and present an approach for detecting repetitive behavioral patterns in order to determine trend-setters and followers. Furthermore, we prove the effectiveness of combining conceptual scale building with temporal concept analysis in order to investigate life-tracks relative to specific behaviors discovered in online educational platforms.