Abstract:
E-learning is increasingly becoming the preferred delivery
mode in learning institutions as it allows any time anywhere learning.
However, content delivery, access, distribution and personalization are
still a challenge. Moreover, ambiguity of students during decision making
for their preferred courses has not been addressed. This paper proposes
an adaptive e-learning model, an architecture for the adaptation
of learning course materials considering students’ profiles and their context
information. Integration of fuzziness with processes of customization
and selection of adequate material for the user creates a chance to build
truly personalized and adaptive systems. This adaptive model is helpful
in recommending course materials to students or adapting them depending
on their context. It complements instructors’ efforts in the delivery
of learning materials relevant to their personal profiles. An AeLModel
architecture is presented taking a full advantage of ontology, tagging,
and users’ feedback represented with linguistic descriptors and quantifiers.
A prototype was developed and tested using 20 students in a class
to assess this model’s usability in addition to its adherence to content
adaptation, resulting in a 77% of acceptance. It is recommended for this
to be used in improving learning processes.
