Usage Pattern Recognition in Student Activities

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011.This paper presents an approach of collecting contextualized attention metadata combined from inside as well as outside a LMS and analyzing them to create feedback about the student activities for the teaching staff. Two types of analyses were run on the collected data: first, key actions were extracted to identify usage patterns and tendencies throughout the whole course and then usage statistics and patterns were identified for some key actions in more detail. Results of both analyses were visualized and presented to the teaching staff for evaluation.The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007- 2013) under grant agreement no 231396 (ROLE project). Work was also partially funded by the Learn3 project (TIN2008-05163/TSI), the eMadrid project (S2009/TIC-1650), and the Acción Integrada DE2009-0051

NetLearn: Social network analysis and visualizations for learning

The most valuable and innovative knowledge is hard to find, and it lies within distributed communities and networks. Locating the right community or person who can provide us with exactly the knowledge that we need and who can help us solve exactly the problems that we come upon, can be an efficient way to learn forward. In this paper, we present the details of NetLearn; a service that acts as a knowledge filter for learning. The primary aim of NetLearn is to leverage social network analysis and visualization techniques to help learners mine communities and locate experts that can populate their personal learning environments