Analysis of Relations in eLearning

Authors:
Pavla Drádilová;Jan Martinovic;Katerina Slaninová;Václav Snáel
Affiliations:
-;-;-;-
Venue:
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Year:
2008

Citing 4
Cited 3

Term-weighting approaches in automatic text retrieval

Information Processing and Management: an International Journal
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)

Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Relation Analysis in eLearning

CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
Developing an Information System for Monitoring Student's Activity in Online Collaborative Learning

CISIS '08 Proceedings of the 2008 International Conference on Complex, Intelligent and Software Intensive Systems

Evolution of Professional Ethics Courses from Web Supported Learning towards E-Learning 2.0

EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Social learning network analysis model to identify learning patterns using ontology clustering techniques and meaningful learning

Computers & Education
Analysis of students' behaviour based on participation and results achieved in Wiki-based team assignments

Proceedings of the 6th Balkan Conference in Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is focused on searching for socials networks in eLearning system data. This data does not create a social network from the beginning, but contains records about student activity in an eLearning system. There are latent ties in these collections, which we also interpret as social network. We discovered these latent ties via clustering methods and, consequently, we used methods for social network analyses. The social networks we analyzed in this paper are represented by groups of students who have similar contacts and interact in similar social circles.