Ontology and Rule-Based Recommender System for E-learning Applications (original) (raw)
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The rapid growth of Internet technology and the explosion of educational resources, show the increasing importance of e-learning systems. Despite the importance of these systems, they suffer from the enormous learning materials. In recent years, recommender systems appeared to improve the quality of learning. Such systems were used in learning systems to provide the facilities during the learning process and help learners with a more accurate learning. Different recommendation techniques such as collaborative filtering, content based and the hybrid filtering were employed for e-learning domain. In addition to the importance of learner's needs in the learning process, also the training method for recommended learning materials should be important in this learning process. This paper aims to develop the knowledge based personalized e-learning recommendation system based on ontology. Furthermore, this study discusses about appropriate recommendation technique based on learning syst...
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E-learning has become an active field of research with a lot of investment towards web-based delivery of personalised learning contents to learners. Some issues of e-learning arise from the heterogeneity and interoperability of learning content to suit learner’s style and preferences in order to improve the e-learning environment. Hence, this paper developed an ontology-based personalised recommender system that is needed to recommend suitable learning contents to learners using collaborative filtering and ontology. A pre-test is carried out for users in order to segment them in learning categories to suit their skill level. The learning contents are structured using ontology; and collaborative filtering is used to collects preferences from many users and then recommending the highest rated contents to users. The system is implemented using JAVA programming language with Structured Query Language (MySQL) as database management system. Performance evaluation of the system is car...
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E-learning offers advantages for E-learners by making access to learning objects at any time or place ,very fast, just-in-time and relevance, However, with the rapid increase of learning objects and it is syntactically structured it will be time-consuming to find contents they really need to study, in this paper we design and implementation of knowledge-based industrial reusable, interactive web-based training and use semantic web based e-learning to deliver learning contents to learner in flexible, interactive, adaptive way. The semantic and recommendation and personalized search of Learning objects is based on comparison of the learner profile and learning objects to determine a more suitable relationship between learning objects and learner profiles. Therefore, it will advise the e-learner with most suitable learning objects using the semantic similarity.
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The significant amount of information available on the web has led to difficulties for the learner to find useful information and relevant resources to carry out their training. The recommender systems have achieved significant success in the area of e-commerce, they still have difficulties in formulating relevant recommendations on e-learning resources because of the different characteristics of learners. Most of the existing recommendation techniques do not take these characteristics into account. This problem can be mitigated by including learner information in the referral process. Currently many recommendation techniques have cold start problems and classification problems. In this paper, we propose an ontology-based collaborative filtering recommendation system for recommending learners' online learning resources based on a decision algorithm (DA). In our approach, ontology is used to model and represent domain knowledge about the learner and learning resources. Our approa...
Chapter 13 A Personalized Learning System Based on Semantic Web Technologies
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Today’s Web is an important learning technology platform. Its’ underlying technologies that is named as e-learning technologies has made it a successful environment in particular for the creation and presentation of learning resources. Especially, today’s e-learning technologies are aimed at creation of searchable, reusable learning resources on the web. However, an e-learning application should recommend students learning resources that are easily understandable according to their level of the knowledge and interesting enough to keep the students’ attention. So, adaptivity or personalization is key characteristic of today’slearning applications and e-learning researches. Currently, the Semantic Web can be exploited as a very suitable platform for implementing a personalized e-learning system. Semantic Web is based on ontology technology. Ontology is a ABSTRACT
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This paper describes the design and implementation of a recommender application framework which aims to simplify development of ontology-based recommender applications over the Semantic Web data. Recommender system is a type of system that generates meaningful recommendations to support user’s decision. Development of recommender system for the Semantic Web data typically requires ontology, rules and rule-based inference engine to be applied over the RDF data. To facilitate development of recommender applications, our application framework introduces a recommendation template that is a specific form of rule language that provides high-level abstraction in generating recommendations. Recommendation rules can be created based on the template using recommendation editor to hide complexity of rule language syntax. The framework proposes two implementation approaches for generating recommendation results based on the recommendation rules: rule-based reasoner and SPARQL-based implementati...