This paper presents a systematic literature review of the coordinated use of Learning Analytics and Computational Ontologies to support educators in the process of academic performance evaluation of students. The aim is to provide a general overview for researchers about the current state of this relationship between Learning Analytics and Ontologies, and how they have been applied in a coordinated way. We selected 31 of a total of 1230 studies related to the research questions. The retrieved studies were analyzed from two perspectives: first, we analyzed the approaches where researchers used Learning Analytics and Ontologies in a coordinated way to describe some Taxonomy of Educational Objectives; In the second perspective, we seek to identify which models or methods have been used as an analytical tool for educational data. The results of this review suggest that: 1) few studies consider that student interactions in the Learning Management System can represent students’ learning experiences; 2) most studies use ontologies in the context of learning object assessment to enable learning sequencing; 3) we did not identify methods of evaluation of academic performance guided by Taxonomies of Educational Objectives; and 4) no studies were identified that report the coordinated use of Learning Analytics and Computational Ontologies, in the context of academic performance monitoring. Thus, we identify future directions of research such as the proposal of a new model of evaluation of academic performance.
The paper aims to present research results on using Web 2.0 tools for learning personalisation. In the work, personalised Web 2.0 tools selection method is presented. This method takes into account student's learning preferences for content and communication modes tailored to the learning activities with a view to help the learner to quickly and accurately find the right educational tools, and to implement this method in prototype of knowledge-based recommender system. In the research, first of all, personalised e-learning technological peculiarities i.e. recommender systems applications for learning personalisation and those systems components were investigated. After that, selection methods for Web 2.0 tools suitable for implementing learning activities were analysed. The novel method of integrating Web 2.0 tools into personalised learning activities according to students learning styles was created, and prototype of the recommender system that implements the method proposed was developed. Finally, the expert evaluation of the developed system prototype that implements the method proposed was performed.