While virtual learning environments (VLEs) present several advantages, such as space-time flexibility, they are still not including proper opportunities and resources for students to engage in collaborative activities with their peers. Recent approaches, for example, are based on resources that are not standard for VLEs or usual for students. Thus, their integration with VLEs is not simple. This paper conducted a theoretical investigation to identify strategies that could induce collaborative behaviours in students. These strategies were implemented as learning objects running in a VLE and a quasi-experimental research design was conducted with 133 students. The results show that the approach promotes collaborative interactions between students and also tend to improve their learning outcomes. Moreover, learning objects use a conceptualization that is already established over the e-learning community, simplifying their integration with VLEs.
Content personalization in educational systems is an increasing research area. Studies show that students tend to have better performances when the content is customized according to his/her preferences. One important aspect of students particularities is how they prefer to learn. In this context, students learning styles should be considered, due to the importance of this feature to the adaptivity process in such systems. Thus, this work presents an efficient approach for personalization of the teaching process based on learning styles. Our approach is based on an expert system that implements a set of rules which classifies learning objects according to their teaching style, and then automatically filters learning objects according to students' learning styles. The best adapted learning objects are ranked and recommended to the student. Preliminary experiments suggest promising results.
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.
The technological resources used for pedagogical innovation in the form of distance education have increasingly been incorporated into face-to-face education. This article describes the experience of the Federal University of Lavras - Brazil - with new ways to apply technology in face-to-face undergraduate courses. This paper presents (i) the strategy for the selection of course content, which was premised on the diversification of areas of knowledge and on promoting the permanent incorporation of the resources developed in the teaching-learning process, (ii) the organization of the production process of Learning Objects based on the Scrum method, (iii) the set of best practices, inspired by the management of agile software development, as well as the contextual motivation of its use.
The paper aims to analyse several scientific approaches how to evaluate, implement or choose learning content and software suitable for personalised users/learners needs. Learning objects metadata customisation method as well as the Method of multiple criteria evaluation and optimisation of learning software represented by the experts' additive utility function are analysed in more detail. The value of the experts' additive utility function depends on the learning software quality evaluation criteria, their ratings and weights. The Method is based on the software engineering Principle which claims that one should evaluate the learning software using the two different groups of quality evaluation criteria - `internal quality' criteria defining the general software quality aspects, and `quality in use' criteria defining software personalisation possibilities. The application of the Method and Principle for the evaluation and optimisation of learning software is innovative in technology enhanced learning theory and practice. Application of the method of the experts' (decision makers') subjectivity minimisation analysed in the paper is also a new aspect in technology enhanced learning science. All aforementioned approaches propose an efficient practical instrumentality how to evaluate, design or choose learning content and software suitable for personalised learners needs.