Highlighting Trend-Setters in Educational Platforms by Means of Formal Concept Analysis and Answer Set Programming - Artificial Intelligence for Knowledge Management Access content directly
Conference Papers Year : 2018

Highlighting Trend-Setters in Educational Platforms by Means of Formal Concept Analysis and Answer Set Programming

Sanda Dragoş
  • Function : Author
  • PersonId : 1040278
Diana Şotropa
  • Function : Author
  • PersonId : 1040279
Diana Troancă
  • Function : Author
  • PersonId : 1040277

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.
Fichier principal
Vignette du fichier
469211_1_En_5_Chapter.pdf (1.07 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01950007 , version 1 (10-12-2018)

Licence

Attribution

Identifiers

Cite

Sanda Dragoş, Diana Şotropa, Diana Troancă. Highlighting Trend-Setters in Educational Platforms by Means of Formal Concept Analysis and Answer Set Programming. 4th IFIP International Workshop on Artificial Intelligence for Knowledge Management (AI4KM), Jul 2016, New York, NY, United States. pp.71-92, ⟨10.1007/978-3-319-92928-6_5⟩. ⟨hal-01950007⟩
64 View
51 Download

Altmetric

Share

Gmail Facebook X LinkedIn More