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Volume 16 / Issue 16

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DOI:   10.3217/jucs-016-16-2252


User Context and Personalized Learning: a Federation of Contextualized Attention Metadata

Valentin Butoianu (Université Paul Sabatier, France)

Philippe Vidal (Université Paul Sabatier, France)

Katrien Verbert (K.U. Leuven, Belgium)

Erik Duval (K.U. Leuven, Belgium)

Julien Broisin (Université Paul Sabatier, France)

Abstract: Nowadays, personalized education is a very hot topic in technology enhanced learning (TEL) research. To support students during their learning process, the first step consists in capturing the context in which they evolve. Users typically operate in a heterogeneous environment when learning, including learning tools such as Learning Management Systems and non-learning tools and services such as e-mails, instant messaging, or web pages. Thus, user attention in a given context defines the Contextualized Attention Metadata (CAM). Various initiatives and projects allow capturing CAMs in a knowledge workers’ environment not only in the TEL area, but also in other domains like Knowledge Work Support, Personal Information Management and Information Retrieval. After reviewing main existing approaches according to some specific criteria that are of main interest for capturing and sharing user contexts, we present in this paper a framework able to gather CAMs produced by any tool or computer system. The framework is built on the Web-Based Enterprise Management (WBEM) standard dedicated to system, network and application management. Attention information specific to heterogeneous tools are represented as a unified and extensible structure, and stored into a central repository compliant with the above-mentioned standard. To facilitate access to this attention repository, we introduced a middleware layer composed of two dynamic services: the first service allows users to define the attention data they want to collect, whereas the second service is dedicated to receive and retrieve the traces produced by computer systems. An implementation for collecting and storing CAM data generated by the Ariadne Finder and Moodle validates our approach.

Keywords: contextualized attention metadata, technology enhanced learning

Categories: L.2.0, L.2.2, L.3.0, L.3.6