This article presents an innovative approach to developing a strategy for situational awareness in an adaptive gamification framework within the context of Collaborative location-based collecting systems (CLCS). The proposed approach involves incorporating five key factors that represent user behavior and context in the adaptive gamification process for CLCS. These factors include player preferences, player status, gamified activities, groupware activities, and project goal status. Each factor is crucial in describing various aspects of the framework and groupware interaction that players should be aware of during their game experience. By acquiring knowledge about these five axes, users can analyze and react appropriately to various situations. This modeling approach enables the early development of awareness and facilitates decision-making. Ultimately, this article serves as a useful guide for improving existing frameworks and enhancing the overall user experience through situational awareness.

Many citizen science projects that carry out survey tasks based on location require that the territory to be studied be fragmented into smaller areas with the objectives of, on the one hand, keeping a record of the level of coverage of the regions, and on the other hand, to present spatially bounded objectives to the volunteers of the project. In some cases the sampling areas are related to a terrain feature, such as when surveying the shores of rivers and lakes. Therefore, the afore-mentioned segmentation must respect the topographical shape of the geographical object to be studied (river or lake).In this work, this type of tessellation is defined as topographic tessellation (TT). Aiming at building the TT, indicating the distance it should have from the shore and the specific measurements of each smaller area is needed. This article presents a framework for the automatic generation of topographic tessellations, which are sets of disjoint and adjacent polygons that form a mosaic following the shape of a georeferenced geometry, and builds a new geographical layer. This tool is useful for spatial-task asignment decision-making.

This article proposes an adaptive gamification approach based on a Multi-Criteria Recommendation System (MCRS) for Collaborative Location-based Collecting Systems, adapting the gamification to each user, taking into account her preferences and the project’s objectives as a multi-criteria scenario. Specifically, the potentially recommended items are dynamically generated gamification elements, and the recommendation criteria are defined considering two points of view: user preferences and project objectives. Finally, the article includes an evaluation of the proposal and then a discussion of the results.

Collaborative location-based collecting systems (CLCS) are a particular case of collaborative systems where a community of users collaboratively collect geo-referenced data. Each CLCS sets its territory coverage objectives, commonly defined as to guarantee that all the af-fected territory is surveyed with a particular coverage criterium. This paper presents a three-step pipeline to recommend the subareas that re-quire observations dynamically. The first step generates a disjoint and adjacent set of areas -a mesh- covering the sampling territory. The sec-ond step sets a priority and coverage objective for each area. Finally, the third step considers the project’s objectives and the area coverage situation to recommend the areas that need surveys. The output of this last step is an input for a user-task distribution process where the user’s profile is taken into account. Moreover, an example of meshing strategy and task generation is proposed.

Gamification is a widely used resource to engage and retain users. It is about the use of game elements and mechanics in systems and domains that are not naturally games. Nevertheless, the usage of gamification does not always achieve the expected results due to the too much generalized approach that makes invisible the different motivations, characteristics and playing styles among the players. Currently, research on adaptive gamification deals with the gamification that each particular user needs at a particular moment, adapting gamification to users and contexts. Collaborative location-based collecting systems (CLCS) are a particular case of collaborative systems where a community of users collaboratively collect geo-referenced data. This article proposes an adapted gamification approach for CLCS, through the automatic game challenge generation. Particularly a model of user profile considering the space-time behavior and challenge completion, a model for the different types of challenges applicable in CLCS, a model for the CLCS objectives and coverage, and a strategy for the application of Machine Learning techniques for adaptation.

Collaborative 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 two types of behavioral atoms and two types of users’ behavioral patterns.

Collaborative location collecting systems (CLCS) are collaborative systems where users collects location-based data. When these systems are gamified and aim to adapt the game elements to each user, it may require a user traveling behavior profile. This work presents two approaches of traveling user behavior profiling: a raw series built up with categorical data that describes the user's activity in a period, and a timed series that is an enhanced version of the first that includes a representation of the non-activity time frames. The profiling of user traveling behavior can be used in adaptive gamification strategies. The approach is evaluated over a behavioral atoms dataset based on a year of Foursquare check-ins. The results showed that both approaches reflex different aspects of traveling user behavior, and also both could be used in a complementary manner.

Mass 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.