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 tag-based recommendations has been used and evaluated in a programming tutoring system.

The complexity of today’s learning processes and practices entails various challenges. It is becoming much harder for teachers to observe, control, and adjust the learning process. Moreover, contemporary teaching is enhanced with different technologies and systems that not only support information-transfer, but also make this process more effective. In this paper we present the Programming Tutoring System (ProTuS), which provides smart and interactive content, personalization options, adaptive features, and learning analytics as a support for users engaged in learning complex cognitive skills. Our contribution in this paper is twofold, conceptual and empirical. The paper presents the interactive learning analytics component developed in ProTuS and the results from the empirical study. The study shows that students find adaptive learning systems to be useful in monitoring progress, promoting reflective practices, and receiving feedback to better understand their actions and learning strategies.

Learning analytics aims to analyze data from students and learning environments to support learning at different levels. Although learning analytics is a recent field, it reached a high level of maturity, especially in its applications for higher education. However, little of the research in learning analytics targets other educational levels, such as high school. This paper reports the results of a systematic literature review (SLR) focused on the adoption of learning analytics in high schools. More specifically, the SLR followed four steps: the search, selection of relevant studies, critical assessment, and the extraction of the relevant field, which included the main goals, approaches, techniques, and challenges of adopting learning analytics in high school. The results show that, in this context, learning analytics applications are focused on small-scale initiatives rather than institutional adoption. Based on the findings of this study, in combination with the literature, this paper proposes future directions of research and development in order to scale up learning analytics applications in high schools.