A Framework for ‍Classification, Rating, and Extraction from Learners’ Annotations to Produce Learning Contents in E-Learning Systems

Nowadays e-learning has an important role as a collaborative learning environment in education. Obviously one of the most important challenges in e-learning is to produce appropriate learning contents for learners. One solution could be selecting and edit appropriate annotations being placed by learners. Due to the possibility of adding annotations to a specific learning content, exploiting learners’ annotations can help course author to improve his/her learning content. Regarding to concepts’ ontology and contents’ annotations, it is possible to edit certain contents in content hierarchy. Furthermore, it is possible to create learning contents by selecting high rated learners’ annotations and presenting them to other learners. Being an implicit method related content annotation that can be easily edited or removed are considered as the advantages of the proposed method of using annotations for editing contents. In this paper a framework for using learners’ annotations for selecting and editing learning contents in e-learning systems is presented. The details of annotations classification and method of rating annotations will be discussed. In addition, the proposed solution is tested and advantages of annotations are analyzed in order to produce feedbacks for authors and the conclusions are shown