Mining web analytics data for information wikis to evaluate informal learning

Heba M. Ismail, Boumediene Balkhouche, Saad Harous

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Information wikis and especially Wikipedia have become one of the most attractive environments for informal learning. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being required to follow a predefined learning path in line with the constructivist learning theory. Link-based navigation and keyword-based search methods used on Wikipedia and similar information wikis suffer from many limitations. In our paper, we present an effective recommendation system that provides easier and faster access to relevant content on Wikipedia to support informal learning. In addition, we evaluate the impact of personalized content recommendations on informal learning from Wikipedia and show how web analytics data can be used to get an insight on informal learning in similar environments.

Original languageEnglish
Pages (from-to)125-148
Number of pages24
JournalInternational Journal of Engineering Pedagogy
Volume10
Issue number1
DOIs
Publication statusPublished - 2020

Keywords

  • Evaluation
  • Informal learning
  • Information filtering
  • Information wikis
  • Personalized content recommendations
  • Recommender systems
  • Web analytics
  • Wikipedia

ASJC Scopus subject areas

  • Education
  • Engineering(all)

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