No abstract

Personalization of the e-learning systems according to the learner's needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tagbased recommendations has been used and evaluated in a programming tutoring system.

This paper considers the data science and the summaries significance of Big Data and Learning Analytics in education. The widespread platform of making high‐quality benefits that could be achieved by exhausting big data techniques in the field of education is considered. One principal architecture framework to support education research is proposed.

No abstract

No abstract

No abstract

The aim of Semantic Web is to provide distributed information with well-defined meaning, understandable for humans as well as machines. E-learning is an important domain which can be benefited from the Semantic Web technology. Ontologies, as a building structure of Semantic Web, will fundamentally change the way in which e-learning systems are constructed. The explicit conceptualization of system components in a form of ontology facilitates knowledge sharing, knowledge reuse, communication and collaboration among system components, and construction of intensive and expressive systems. In previous research, we implemented tutoring system named Protus (PRogramming TUtoring System) that is used for learning basic concepts of Java programming language. Protus uses principles of learner style identification and content recommendation for course personalization. The new version of the system called Protus 2.0, supported by several ontologies, as well as examples of its usage for performing personalization are presented in this paper. Architecture of new system extends the usage of Semantic Web concepts, where the representation of each Protus 2.0 component is made by a specific ontology, making possible a clear separation of the tutoring system components and explicit communication among them. [Projekat Ministarstva nauke Republike Srbije, br. III47003]

A way to improve the effectiveness in e-learning is to offer the personalized approach to the learner. Adaptive e-learning system needs to use different strategies and technologies to predict and recommend the most likely preferred options for further learning material. This can be achieved by recommending and adapting the appearance of hyperlinks or simply by recommending actions and recourses. This paper presents an idea for integration of such recommender system into existing web-based Java tutoring system in order to provide various adaptive programming courses.