Learning Content Recommendation for Visual Basic.Net Programming Language based on Ontology

Abstract

Nowadays, the quality of learning and the expansion of education technology, motivate the researchers to work on learning area more than before. Problem statement: With the rapid advance of learning contents on the web and also the variety of learning books, finding suitable ones has become a very difficult and complicated task for learners. Approach: This study aims to propose a learning system includes the semantic recommender system. Students can employ this application to learn learning content at anywhere. This system works based on the learner’s knowledge level and also the learner’s request that system asks from the learner at the beginning. Learner will be able to find and learn the right learning materials to reach their request. Finally, all changes about learner will store in the learner model in the ontology. The proposed architecture comprises some subsystems and components. One of the most important of subsystems is a knowledge based system, which covers the ontology which called VBnet ontology. This ontology consists of three parts; LearnerModel, Domain Concept and Learning Material. Moreover, we define two other subsystems; Learner performance evaluation, recommendation system and some modules; Availability checker, Knowledge evaluator, Exam generator, Request analyzer and user interface. Results: Considering to scope of research we develop the ontology for Visual Basic.Net programming language and describe all available classes and subclasses step by step. Also we create some query by SPARQL and show the information retrival from VB ontology. Conclusion: This system can help to student to learn materials of Visual Basic.Net with the good quality without the place dependency.