Mining Web Analytics Data for Information Wikis to Evaluate Informal Learning

Authors

  • Heba M. Ismail Dept. of Computer Science and Software Engineering College of Information Technology United Arab Emirates University P.O Box 15551 Al Ain, United Arab Emirates
  • Boumediene Balkhouche UAE University
  • Saad Harous Dept. of Computer Science and Software Engineering College of Information Technology United Arab Emirates University P.O Box 15551 Al Ain, United Arab Emirates https://orcid.org/0000-0001-6524-7352

DOI:

https://doi.org/10.3991/ijep.v10i1.11713

Keywords:

Information filtering, Information Wikis, Informal Learning, Personalized Content Recommendations, Recommender systems, Wikipedia, Evaluation, Web Analytics

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 con-struct 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 in-sight on informal learning in similar environments.

Author Biographies

Heba M. Ismail, Dept. of Computer Science and Software Engineering College of Information Technology United Arab Emirates University P.O Box 15551 Al Ain, United Arab Emirates

Heba Ismail is a PhD candidate in Information Technology. She worked on multiple research projects related to text mining, social network analysis, information retrieval, and natural language processing. Currently, her research interest is focused on user modeling, recommender systems for technology enhanced learning, and personalized learning systems.

Boumediene Balkhouche, UAE University

Boumediene Balkhouche currently works at the Primitive Learning, École Buissonnière. Boumediene does research in Algorithms, Programming Languages and Software Engineering. Their current project is 'personalized learning.' Email: b.balkhouche@uaeu.ac.ae

Saad Harous, Dept. of Computer Science and Software Engineering College of Information Technology United Arab Emirates University P.O Box 15551 Al Ain, United Arab Emirates

Saad Harous obtained his PhD in computer science from Case Western Reserve University, Cleveland, OH, USA in 1991. He has more than 30 years of experience in teaching and research in three different countries: USA, Oman and UAE. He is currently a Professor at the College of Information Technology, in the United Arab Emirates University. His teaching interests include programming, data structures, design and analysis of algorithms, operating systems and networks. His research interests include parallel and distributed computing, P2P delivery architectures, wireless networks and the use of computers in education and processing Arabic language. He has published more than 150 journal and conference papers. He is an IEEE senior member. Email: harous@uaeu.ac.ae

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Published

2020-01-27

How to Cite

Ismail, H. M., Balkhouche, B., & Harous, S. (2020). Mining Web Analytics Data for Information Wikis to Evaluate Informal Learning. International Journal of Engineering Pedagogy (iJEP), 10(1), pp. 125–149. https://doi.org/10.3991/ijep.v10i1.11713

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Papers