Semantic web based adaptive E-Learning triggered through short message services

As E-Learning Systems are gaining more and more popularity; this paper proposes an adaptive E-Learning System based on semantic web technologies. Initially we calculate the users degree of interest towards different course topics based on SMS triggered E-Assessments which could easily be conducted in classrooms or in distant mode. We then classify the different users based on the assessment score. Now it is possible to calculate the semantic similarity between the user evaluations of E-Assessments based on concepts in domain ontology. According to the similarity measures between different users it is possible to classify them in different clusters and finally adaptive recommendations for individual users can be provided based on nearest neighborhoods in the form of resources, assignments and further E-Assessments.