A KNN-Based Recommendation System for Adaptive Collaborative Learning

Jalal Lahiassi (), Oussama Elwarraki, Souhaib Aammou, Youssef Jdidou and Hind Ben Rahmoun
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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

A chapter in Technological Innovations for Sustainable Development, 2025, pp 382-393 from Springer

Abstract: 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

Keywords: Computer-Supported Collaborative Learning (CSCL); Recommendation System; K-Nearest Neighbors (KNN) (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-06725-8_32

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DOI: 10.1007/978-3-032-06725-8_32

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