A Social Profile-Based Recommendation Architecture for E-Learning Systems

Xola Ntlangula, Wai Sze Leung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The increased reliance of education institutions on e-learning systems to provide learning and teaching activities and resources has prompted a need for innovative strategies that deliver effective learning to students. One approach being considered involves enhancing personalized learning experiences through the implementation of personalized learning paths. Personalized learning paths have proven highly beneficial for students; however, they are predominantly guided by learning objectives rather than the unique attributes of each student. Additionally, the inflexibility observed in e-learning systems poses a challenge in integrating customization options. This paper proposes an architecture for a recommendation system that creates personalized learning paths from a student's profile. The resulting architecture enhances an e-learning system's personalization by using external student data to create a more comprehensive student profile, thereby improving the system’s ability to generate learning paths and deliver technology-enhanced learning experiences.

Original languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the 2024 Computing Conference
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages330-343
Number of pages14
ISBN (Print)9783031622762
DOIs
Publication statusPublished - 2024
EventScience and Information Conference, SAI 2024 - London, United Kingdom
Duration: 11 Jul 202412 Jul 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1017 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceScience and Information Conference, SAI 2024
Country/TerritoryUnited Kingdom
CityLondon
Period11/07/2412/07/24

Keywords

  • Personalized learning paths
  • Recommendation systems
  • Social profile

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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