188/4 E-Commerce Group
Institute of Software Technology and Interactive Systems
Vienna University of Technology
Favoritenstrasse 9-11/188, A-1040 Vienna, Austria

Mining User Knowledge in Learning Networks

Type: 
Speech with proceedings
Abstract: 
Intelligent tutoring systems rely on learner models to recommend useful learning resources. Learner models suffer from incomplete and inaccurate information about learners. Learning networks are web-based platforms where people share knowledge and collaboratively solve problems, which triggers learning mechanisms. In this paper, we present an approach that calculates learners┬┤ competence scores based on their contributions and social interactions in a learning network. This may result in more complete and accurate learner models, which allow more suitable recommendations of learning resources. For evaluation, we conducted an experiment with 14 master students at university. The results show that our approach tends to underestimate competences, while it calculates 54% of the scores accurately. Student feedback suggests using the approach to recommend future courses as well as forming student groups.
Hidden Keywords: 
Department Focus: 
Business Informatics
TU Focus: 
Information and Communication Technology
Proceedings: 
Local Proceedings of the 10th International Conference BIR2011
Publisher: 
Riga Technical University
Year: 
2011
ISBN: 
ISBN: 978-9984-30-197-6
Pages: 
267 - 274
Accepted: 
Acceptance undecided
Invited: 
Reference: 
<u>M. Hochmeister</u>: <br>"<i>Mining User Knowledge in Learning Networks</i>"; <br>Talk: Perspectives in Business Informatics Research, BIR2011, Riga, Latvia; 10-06-2011 - 10-08-2011; in: "<i>Local Proceedings of the 10th International Conference BIR2011</i>", L. Niedrite, R. Strazdina, B. Wangler (ed.); Riga Technical University, Riga (2011), ISBN: 978-9984-30-197-6; 267 - 274.<br><br> <a href="https://publik.tuwien.ac.at/showentry.php?ID=202184&lang=2" class="publist"><i>More information</i></a><br><br>
Abstract German: 
Intelligent tutoring systems rely on learner models to recommend useful learning resources. Learner models suffer from incomplete and inaccurate information about learners. Learning networks are web-based platforms where people share knowledge and collaboratively solve problems, which triggers learning mechanisms. In this paper, we present an approach that calculates learners┬┤ competence scores based on their contributions and social interactions in a learning network. This may result in more complete and accurate learner models, which allow more suitable recommendations of learning resources. For evaluation, we conducted an experiment with 14 master students at university. The results show that our approach tends to underestimate competences, while it calculates 54% of the scores accurately. Student feedback suggests using the approach to recommend future courses as well as forming student groups.