IDEAS home Printed from https://ideas.repec.org/h/spr/lnichp/978-3-032-06725-8_32.html

A KNN-Based Recommendation System for Adaptive Collaborative Learning

In: Technological Innovations for Sustainable Development

Author

Listed:
  • Jalal Lahiassi

    (Abdelmalek Essaadi University, TIMS Laboratory, Faculty of Science)

  • Oussama Elwarraki

    (Abdelmalek Essaadi University, TIMS Laboratory, Faculty of Science)

  • Souhaib Aammou

    (Abdelmalek Essaadi University, TIMS Laboratory, Faculty of Science)

  • Youssef Jdidou

    (Ecole Marocaine Des Sciences de L’Ingénieur, Laboratory of Intelligent Systems and Applications (LSIA))

  • Hind Ben Rahmoun

    (Ecole Normale Supérieure Tétouan, Abdelmalek Essaadi University)

Abstract

In the context of Computer-Supported Collaborative Learning (CSCL), it is essential to propose activities tailored to students’ profiles and interactions. This study explores the use of a recommendation system based on the K-Nearest Neighbors (KNN) algorithm to suggest relevant collaborative activities to learners. Our approach analyzes student characteristics (learning level, forum participation, activity preferences, engagement) to identify similar profiles and recommend appropriate activities. After normalizing and structuring this data, we apply KNN to determine the K most similar students and suggest activities based on their past experiences. An experiment was conducted with 60 Master's students, divided into two groups: one receiving personalized recommendations and a control group without recommendations. The results indicate a 25% increase in participation rates for the group benefiting from recommendations. Additionally, a t-test analysis revealed a statistically significant difference (p

Suggested Citation

  • Jalal Lahiassi & Oussama Elwarraki & Souhaib Aammou & Youssef Jdidou & Hind Ben Rahmoun, 2025. "A KNN-Based Recommendation System for Adaptive Collaborative Learning," Lecture Notes in Information Systems and Organization, in: Badr-Eddine Boudriki Semlali & Ikram Ben Abdel Ouahab & Fabio Angeletti (ed.), Technological Innovations for Sustainable Development, pages 382-393, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-06725-8_32
    DOI: 10.1007/978-3-032-06725-8_32
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lnichp:978-3-032-06725-8_32. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.