Mounia Abik

C-MADAR Learning: Cloud based learning environment

With a huge number of resources, users and services, E-Learning systems may encounter some challenges regarding dynamic concurrency, access to resources or handling the rapid content storage. Also adds to these issues, optimization and control of resources and costs. Through cloud computing, we can not only answer these needs but also provide an architecture that guarantees better availability, broad scalability, and a higher quality of service. This paper explores the cloud computing advantages in the learning field by the integration of several technologies such as virtualization, SOA (Service Oriented Architecture) and grid computing in order to improve our pervasive learning environment: MADAR Learning.

Knowledge construction in the Connectivist Learning Environment — CLE

Technological advances have brought great changes in all areas, including education. The distance learning evolution (D-learning, E-learning and M-Learning) has prompted the adoption of the most interesting pedagogical approaches such as constructivism and social-constructivism... The adoption of these pedagogical trends improved the quality of learning by providing the personalization of learning and collaborative learning. Through this technological evolution and with the development of the Web 2.0, a new pedagogical approach called Connectivism has emerged [1]. Its a promising pedagogical approach that covers learning in heterogeneous communities (humans or not) and is based on the contribution of new technologies. However, Web 2.0 is not efficient to reach distributed knowledge in networks in a smart way and where the ability to learn becomes more and more important [2]. The objective of our works is to conceive and implement CLE Connectivist Learning Environment which is a clouded and ubiquitous learning environment. The intended purpose is to adopt connectivism as a pedagogical approach in order to construct knowledge through collaboration between heterogeneous communities composed of humans and intelligent objects.

Platform for Dynamic Adaptation of Learning Content for Pervasive Learning Environments

With the evolution and convergence of wireless technologies, mobile devices and sensors, the mobile and ubiquitous learning have appeared next to the e-learning. Far from being simple extensions of distance learning, they pose several challenges. Most of learning content used in learning platforms are built to be consumed on broadband networks and powerful terminals. However, technological advances have allowed their access over low-flow wireless networks, and terminals with limited resources such as ubiquitous and mobile devices. With this type of access, there is a strong need for learning content adaptation systems. Our work focuses on pervasive learning environments which are the new trends of distant learning and proposes to solve a big range of constraints posed by the use of various technologies in learning. We are interested in studying the various limitations of learning in pervasive environments and the development of a platform for dynamic adaptation of learning content, taking into account the challenges posed by the evolving learning context.

A hashtags dictionary from crowdsourced definitions

Hashtags are user-defined terms used on the Web to tag messages like microposts, as featured on Twitter. Because a hashtag is a textual word, its representation does not convey all the concepts it embodies. Several online dictionaries have been manually and collaboratively built to provide natural language definitions of hashtags. Unfortunately, these dictionaries in their rough form are inefficient for their inclusion in automatic text processing systems. As hashtags can be polysemic, dictionaries are also agnostic to collision of hashtags. This paper presents our approach for the automatic structuration of hashtags definitions into synonym rings. We present the output as a so-called folksionary, i.e. a single integrated dictionary built from everybodys definitions. For this purpose, we achieved a semantic-relatedness clustering to group definitions that share the same meaning.