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Technology has opened the possibility of improving learning environments. However, in the field of medical education, the tools that are used do not provide clear evidence as to whether students are actually learning. Specifically, in the teaching of anatomy, multiple applications have emerged to satisfy the need to access cadaveric material, nevertheless, these lack enriched information on the performance achieved by students and how to adapt learning according to their educational needs. Thus, one of the strategies currently present in this area is gamification. This study implements and uses a gamified educational software platform based on recommender systems and virtual assistants, capable of delivering feedback and strategies to support the appropriation of anatomy knowledge of medical students at the Universidad de La Frontera (UFRO), in the city of Temuco, Chile. Forty-five students participated in the study. The study consisted in the use of various gamified components with artificial intelligence techniques. The main findings of this experience led to the conclusion that the use of gamified components for learning anatomy is a resource that supports student learning. [ABSTRACT FROM AUTHOR]

The objective of this work was to propose a theoretical model of pervasive information architecture, focused on the use of recommendation systems to increase the findability of information in university libraries. This research was developed from a multidisciplinary perspective and it is defined as exploratory research since a bibliographic review is carried out. This review allows obtaining information on the theoretical elements necessary to establish the central base of the research. It is also descriptive, as it seeks to bring solutions to issues that arise in the daily life of university libraries. As a result, a model of pervasive information architecture for university libraries is presented with a focus on improving information findability from the inclusion of recommendation systems. It can be concluded that, due to the wide advance in the development of applications and tools for use in libraries, a notable increase in the level of complexity of the information environments of these institutions is perceived, which from the perspective of information architecture creates the need for more refined research projects, whose aim is to improve the information environments of university libraries, always with the main objective of favoring the experiences of users, and helping them in the processes of finding relevant information, at the precise time and with the highest possible quality. [ABSTRACT FROM AUTHOR]

This paper focuses on developing a method of hybrid context-aware recommender systems, based on ontologies in the environment of the Spatial Data Infrastructure, which will positively impact the effectiveness of decision-making in the tourist applications for mobile users. The method developed has three components: data and semantic framework, other that implements the recommender system and another responsible for displaying the recommendations. The recommender system component implements a spatial, a semantic and collaborative filters. The recommendations generated show the elements of potential interest, these can be displayed in tabular or combined with spatial information. It was found through an experiment and different applied metrics the increase in its effectiveness for mobile users according to their preferences as to the recommendations given in a spatial environment. [ABSTRACT FROM AUTHOR]

This paper presents an architecture for semantic context-aware recommender systems intended to be implemented on organizational settings. We described the use of this architecture in the development of the Find-It! project to validate its utility. The Find-It! project consider contextual factors to recommend people, knowledge objects, places, activities, events, technological resources and services to the visitors of an organization. The architecture defines an inference module to filter the items subject of recommendation by using different contextual dimensions that are modeled in a single ontologies, the novelty of an item, users' perception of recommendations and contextual conditions considered when they were recommended in previous visits. We also defined a mobile client interface capable to adapt to the user characteristics and device. This client interface counts with implicit and explicit mechanisms to identify the user satisfaction degree with the recommendations received and its quality. Experience obtained with the development of the Find-It! project shows a satisfactory performance. In this way, it is adequately considered to use the architecture to develop a wide range of organizational recommender systems. [ABSTRACT FROM AUTHOR]