Recommender System to Analyze Student's Academic Performance

  • Authors:

  • A. Kaklauskas;E. K. Zavadskas;M. Seniut;V. Stankevic;J. Raistenskis;C. SimkevičIus;T. Stankevic;A. Matuliauskaite;L. Bartkiene;L. Zemeckyte;R. Paliskiene;R. Cerkauskiene;V. Gribniak

  • Affiliations:

  • Vilnius Gediminas Technical University, Vilnius, Lithuania;Vilnius Gediminas Technical University, Vilnius, Lithuania;Vilnius Gediminas Technical University, Vilnius, Lithuania;Semiconductor Physics Institute, Center for Physical Sciences and Technology, Vilnius, Lithuania;Vilnius University, Vilnius, Lithuania;Semiconductor Physics Institute, Center for Physical Sciences and Technology, Vilnius, Lithuania;Semiconductor Physics Institute, Center for Physical Sciences and Technology, Vilnius, Lithuania;Vilnius Gediminas Technical University, Vilnius, Lithuania;Vilnius Gediminas Technical University, Vilnius, Lithuania;Vilnius Gediminas Technical University, Vilnius, Lithuania;Vilnius Gediminas Technical University, Vilnius, Lithuania;Vilnius University, Vilnius, Lithuania;Vilnius Gediminas Technical University, Vilnius, Lithuania

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

A sufficient amount of studies worldwide prove an interrelation linking student learning productivity and interest in learning to physiological parameters. An interest in learning affects learning productivity, while physiological parameters demonstrate such changes. Since the research by the authors of the present article confirmed these interdependencies, a Recommender System to Analyze Student's Academic Performance (Recommender System hereafter) has been developed. The Recommender System determines the level of learning productivity integrally by employing three main techniques (physiological, psychological and behavioral). This Recommender System, developed by these authors, uses motivational, educational persistence and social learning theories and the database of best global practices based on above theories to come up with recommendations for students on how to improve their learning efficiency. The Recommender System can pick learning materials taking into account a student's learning productivity and the degree to which learning is interesting. Worldwide research includes various scientists who conducted in-depth studies on the different and very important areas of physiological measurements and intelligent systems. We did not manage to find any physiological measurements or any intelligent or integrated system that would take physiological parameters of students, analyze their learning efficiency and, in turn, provide recommendations.