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CALMS: A context-aware learning mobile system based on ontologies

Abstract:

The growing use of mobile devices to support the teaching - learning process and their use in storing academic contents on virtual platforms, requires that systems oriented to the mobile learning take into consideration the educational context of students. One of the biggest challenges during the construction of these systems is the modeling of the context and the possibility of including ontology-based approaches in order to obtain a better structure of the information. Thus, this paper presents a mobile learning system that uses a network of ontologies, which allow the representation of the contextual dimensions of a teaching - learning environment (e.g., location, time, user profiles and knowledge areas). In addition, it is possible to take advantage of the use of semantic search (keywords)and route algorithms applied to ontological models due to their expressiveness and extensible architecture. This study looks for providing students and teachers with personalized and relevant academic information in their current context of study. Finally, as a first step towards an empirical evaluation, the proposed system has been tried into a real academic context by means of a quasi-experiment with six students. Here, the results show favorable perceptions from the participants about its usefulness. These results were obtained using the statistical tool ANOVA, and show a positive influence on the academic performance of the students.