International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Courses Recommendation Algorithm Based On Performance Prediction In E-Learning

  • Koffi, Dagou Dangui Augustin Sylvain Legrand (Unite de Formation et de Recherche des Mathematiques et Informatique (UFR-MI), Universite Felix Houphouet-Boigny (UFHB)) ;
  • Ouattara, Nouho (Laboratoire de Recherche en Informatique et Telecommunication (LARIT) Universite Felix Alassane Ouattara (UAO)) ;
  • Mambe, Digrais Moise (Laboratoire de Recherche en Informatique et Telecommunication (LARIT) Universite Nangui Abrogoua (UNA)) ;
  • Oumtanaga, Souleymane (Laboratoire de Recherche en Informatique et Telecommunication (LARIT) Institut National Polytechnique Felix Houphouet-Boigny (INPHB)) ;
  • ADJE, Assohoun (Unite de Formation et de Recherche des Mathematiques et Informatique (UFR-MI), Universite Felix Houphouet-Boigny (UFHB))
  • 투고 : 2021.02.05
  • 발행 : 2021.02.28
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초록

The effectiveness of recommendation systems depends on the performance of the algorithms with which these systems are designed. The quality of the algorithms themselves depends on the quality of the strategies with which they were designed. These strategies differ from author to author. Thus, designing a good recommendation system means implementing the good strategies. It's in this context that several research works have been proposed on various strategies applied to algorithms to meet the needs of recommendations. Researchers are trying indefinitely to address this objective of seeking the qualities of recommendation algorithms. In this paper, we propose a new algorithm for recommending learning items. Learner performance predictions and collaborative recommendation methods are used as strategies for this algorithm. The proposed performance prediction model is based on convolutional neural networks (CNN). The results of the performance predictions are used by the proposed recommendation algorithm. The results of the predictions obtained show the efficiency of Deep Learning compared to the k-nearest neighbor (k-NN) algorithm. The proposed recommendation algorithm improves the recommendations of the learners' learning items. This algorithm also has the particularity of dissuading learning items in the learner's profile that are deemed inadequate for his or her training.

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과제정보

At the location of the Unite de Formation et de Recherche des Mathematiques et Informatique (UFR-MI) of the Universite Felix Houphouet-Boigny (UFHB) and the Laboratoire de Recherche en Informatique et Telecommunication (LARIT).

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