Examinando por Autor "Dalponte Ayastuy, María"
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Acceso Abierto Adaptive Gamification in Collaborative systems, a Systematic Mapping Study(2021) Dalponte Ayastuy, María; Torres, Diego; Fernández, AlejandroMass collaboration mediated by technology is now commonplace (Wikipedia, Quora, TripAdvisor). Online, mass collaboration is also present in science in the form of Citizen Science. These collaboration models, which have a large community of contributors coordinated to pursue a common goal, are known as Collaborative systems. This article introduces a study of the published research on the application of adaptive gamification to collaborative systems. The study focuses on works that explicitly discuss an approach of personalization or adaptation of the gamification elements in this type of system. It employs a systematic mapping design in which a categorical structure for classifying the research results is proposed based on the topics that emerged from the papers review. The main contributions of this paper are a formalization of the adaptation strategies and the proposal of a new taxonomy for gamification elements adaptation. The results evidence the lack of research literature in the study of adapting gamification in the field of collaborative systems. Considering the underlying cultural diversity in those projects, the adaptability of gamification design and strategies is a promissory research field. - Documento de conferencia
Acceso Abierto Adaptive gamification of citizen science projects(2021) Dalponte Ayastuy, María; Torres, DiegoThis article presents the doctoral proposal whose main objective is to develop an adaptation device for the game elements and mechanics in citizen science projects, based on the user behaviour and the community ecosystem. Gamification is a strategy to convene participants to citizen science projects. However, it cannot be generalized because of the different users’ profiles, and so it must be tailored to the users and playing contexts. Specifically, this article introduces the main and secondary objectives, the methodology and the expected results of these doctoral proposal. - Artículo
Acceso Abierto The use of big data in adaptive gamification in collaborative location collecting systems: a case of traveling behavior detection(2021) Dalponte Ayastuy, María; Torres, DiegoCollaborative location collecting systems (CLCS) is a particular case of collaborative systems where a community of users collaboratively collects data associated with a geo-referenced location. Gamification is a strategy to convene participants to CLCS. However, it cannot be generalized because of the different users’ profiles, and so it must be tailored to the users and playing contexts. A strategy for adapting gamification in CLCS is to build game challenges tailored to the player’s spatio-temporal behavior. This type of adaptation requires having a user traveling behavior profile. Particularly, this work is focused on the first steps to detect users’ behavioral profiles related to spatial-temporal activities in the context of CLCS. Specifically, this article introduces: (1) a strategy to detect patterns of spatial-temporal activities, (2) a model to describe the spatial-temporal behavior of users based on (1), and a strategy to detect users’ behavioral patterns based on unsupervised clustering. The approach is evaluated over a Foursquare dataset. The results showed four types of behavioral atoms and nine types of users’ behavioral patterns.