English abstract
The emergence of social media sources has considerably enhanced interaction and
collaboration support in the education field. The integration of such sources can equip
learners with channels and skills to acquire knowledge through sharing of information,
resources, ideas, as well as expressing opinions and comments during the interactions. The
generated contents during interaction support learners' understanding and learning
concepts and this consequently can guide the delivery of personalized learning experience.
However, the utilization of these contents in the current e-learning systems seems
inevitably limited. Besides, the interaction is unreasonable.
The aim of this research is to highlight and address the issue of limited interaction by
opening new space for more participants in addition to the peers and course teacher.
Furthermore, analyzing the generated data during group discussion and social collaboration
has helped to understand the learning concept and learner's characteristics to provide a
personalized learning feature. The research is proposing a framework for personalized elearning with social collaboration support. The framework will be divided into two
techniques namely, adaptive learning and collaborative learning through the use of social
media sources. The framework is meant to provide value-added services for both students
and teachers through an aggregation of information, resources, services and people. This
thesis employs different mining and modeling techniques to extract the required
information about learner's characteristics and learning concepts. The characteristics
targeted in this work are knowledge level, preferences, learning style and social grouping.
The knowledge level is measured on the basis of the richness of the shared messages in
relation to the learning concept. The learning style is identified based on the preferable
learning objects and modeled using Felder-Silverman Learning Style. The identification of
learning style has guided the social grouping of the learners based on social interaction.
The knowledge level and social grouping direct the delivery of the personalized learning
path.
The evaluation of the proposed framework targeted Omani learners by considering two
educational government institutions in the Sultanate of Oman (Sultan Qaboos University
and Ibri College of Technology). The evaluation has been carried out in the form of a case
study and interview resulted in an indication of the enrichment of the contents generated
during social collaborations. The valuable information included in the extracted content
was very useful in understanding the different characteristics of the learners as well as
constructing the domain model. This is an indication of a promising further prospective in
the utilization of generated data during collaboration activity especially for the delivery of
personalization features
