Individualized Follow-up of the Learner Based on the K-Nearest Neighbors (K-NN) Method Embedded in the Retrieval Step of Case Based Reasoning Approach (CBR)

  • Nihad El GhouchEmail author
  • El Mokhtar En-Naimi
  • Abdelhamid Zouhair
  • Mohammed Al Achhab
Conference paper
Part of the Lecture Notes in Intelligent Transportation and Infrastructure book series (LNITI)


Learner follow-up in adaptive learning systems requires real-time decision support approaches, using algorithms to predict learner behavior based on the experiences of other learners (learners already classified in groups). We propose an adaptive learning system architecture using the Felder-Silverman learning style model to detect the initial learning profile for each learner in order to provide a learning path based on his profile and the Incremental Dynamic Case Based Reasoning approach based on the exploitation of learning traces in order to follow and to control the behavior of the learner in an automatic and real-time way through the search for similar past experiences using the K-Nearest Neighbors algorithm.


Adaptive learning system Learning style Learning path Felder and silverman learning style model (FSLSM) Incremental dynamic case based reasoning (IDCBR) Supervised learning machine K-Nearest neighbors method (K-NN) 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nihad El Ghouch
    • 1
    Email author
  • El Mokhtar En-Naimi
    • 1
  • Abdelhamid Zouhair
    • 2
  • Mohammed Al Achhab
    • 3
  1. 1.LIST Laboratory, the Faculty of Sciences and Technologies, UAETangierMorocco
  2. 2.The National School of Applied Sciences, UAEAl-HoceimaMorocco
  3. 3.The National School of Applied Sciences, UAETetouanMorocco

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