Abstract
In the learning environments, users would be helpless without the assistance of powerful searching and browsing tools to find their way. Web-based e-learning systems are normally used by a wide variety of learners with different skills, background, preferences, and learning styles. In this paper, we perform the personalized semantic search and recommendation of learning contents on the learning Web-based environments to enhance the learning environment. Semantic and personalized search of learning content is based on a comparison of the learner profile, that is based on learning style, and the learning objects metadata. This approach needs to present both the learner profile and the learning object description as certain data structures. Personalized recommendation of learning objects uses an approach to determine a more suitable relationship between learning objects and learning profiles. Thus, it may advise a learner with most suitable learning objects. Semantic learning objects search is based on the query expansion of the user query and by using the semantic similarity to retrieve semantic matched learning objects.
Highlights
Learning environment allows learners to access electronic course contents through the network and study them in virtual classrooms
Personalized recommendation of learning objects is based on ontological approach to guide what learning contents a learner should study, i.e. what learning objects a course should have according to learner preference and intention
In order to implement the proposed personalized search of learning objects according to the created ontological models of the learner and learning object, some IMS Learner Information Package Specification corresponding to some IEEE learning object metadata (LOM) [30] standard have been chosen, and the criteria to estimate conformity of LOM to the learner personal profile with the coefficients of importance
Summary
Learning environment allows learners to access electronic course contents through the network and study them in virtual classrooms. It brings many benefits in comparison with conventional learning paradigm, e.g. learning can be taken at any time and at any place. This paper aims to perform the personalized semantic search and recommendation of learning contents on the learning Web-based environments. Semantic and personalized search of learning content is based on a comparison of the learner profile and the learning content description This approach needs to present both the learner profile and the learning object description as certain data structures. Personalized recommendation of learning objects is based on ontological approach to guide what learning contents a learner should study, i.e. what learning objects a course should have according to learner preference and intention
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