Architectural styles and the design of network-based software architectures
Architectural styles and the design of network-based software architectures
MobSOS - A Testbed for Mobile Multimedia Community Services
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
On the Role of Technical Standards for Learning Technologies
IEEE Transactions on Learning Technologies
Social Network Analysis of 45,000 Schools: A Case Study of Technology Enhanced Learning in Europe
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Identification of Learning Goals in Forum-based Communities
ICALT '11 Proceedings of the 2011 IEEE 11th International Conference on Advanced Learning Technologies
Pattern-Based cross media social network analysis for technology enhanced learning in europe
EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
Social learning analytics: five approaches
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
ICALT '12 Proceedings of the 2012 IEEE 12th International Conference on Advanced Learning Technologies
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Current learning services are increasingly based on standard Web technologies and concepts. As by-product of service operation, Web logs capture and contextualize user interactions in a generic manner, in high detail, and on a massive scale. At the same time, we face inventions of data standards for capturing and encoding learner interactions tailored to learning analytics purposes. However, such standards are often focused on institutional and management perspectives or biased by their intended use. In this paper, we argue for Web logs as valuable data sources for learning analytics on all levels of Bronfenbrenner's Ecological System Theory and introduce a simple framework for Web log data enrichment, processing and further analysis. Based on an example data set from a management service for widget-based Personal Learning Environments, we illustrate our approach and discuss the applicability of different analysis techniques along with their particular benefits for learners.