May I suggest? Three PLE recommender strategies in comparison


  • Felix Mödritscher Vienna Universtity of Economics and Business
  • Barbara Krumay Vienna Universtity of Economics and Business
  • Sandy El Helou Ecole Polytechnique Fédérale de Lausanne
  • Denis Gillet Ecole Polytechnique Fédérale de Lausanne
  • Alexander Nussbaumer Graz University of Technology
  • Dietrich Albert Graz University of Technology
  • Ingo Dahn University of Koblenz-Landau
  • Carsten Ullrich Shanghai Jiao Tong University





Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their activities in different learning contexts and even for transitions between these contexts. Hereby, recommender systems which are highly successful in other application areas comprise one relevant technology for supporting learners in PLE-based activities. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts and due to different research approaches dealing with recommenders, we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general.