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Recommender System to Analyze Student’s Academic Performance

  • Arturas KaklauskasEmail author
Chapter
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Part of the Intelligent Systems Reference Library book series (ISRL, volume 81)

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 author along with colleagues 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 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.

Keywords

Academic Performance Physiological Parameter Recommender System Mental Stress Mental Arithmetic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Vilnius Gediminas Technical UniversityVilniusLithuania

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