GVIS: A Facility for Adaptively Mashing Up and Representing Open Learner Models

In this article we present an infrastructure for creating mash up and visual representations of the user profile that combine data from different sources. We explored this approach in the context of Life Long Learning, where different platforms or services are often used to support the learning process. The system is highly configurable: data sources, data aggregations, and visualizations can be configured on the fly without changing any part of the software and have an adaptive behavior based on user's or system's characteristics. The visual profiles produced can have different graphical formats and can be bound to different data, automatically adapting to personal preferences, knowledge, and contexts. A first evaluation, conducted through a questionnaire, seems to be promising thanks to the perceived usefulness and the interest in the tool.

• Type: Journal
• Year: 2010
• Where: ECTEL
• Authors: Luca Mazzola, Riccardo Mazza
• Keywords: Data Aggregations, ECTEL 2010, Learning Process, Machine Learning, Visual Representations