ReGammend V2: Um sistema de recomendação baseado em evidências para personalizar sistemas gamificados.

Abstract

Gamification has been used to optimize user experiences. However, the negative effects associated with this approach have triggered the need for personalization, adjusting game designs and elements to the individual preferences and characteristics of users. In recent years, studies have highlighted the challenges of adapting gamification to users' individual characteristics, proposing personalized solutions and emphasizing the importance of specific adjustments for each profile. However, most of these studies have not explored automation in personalization, underscoring the need to implement features that automate recommendations in gamified systems. To address this challenge, we developed an evidence-based recommendation system for gamified systems, capable of providing automated gamification design recommendations, allowing managers to configure different parameters, such as user typology, taxonomy of game elements, and demographic data to be used in generating recommendations. With its recommendation, management, and visualization functions, the system enables real-time personalization of gamified systems, automatically adjusting designs based on user preferences. This facilitates the recommendation of game elements for different profiles, ensuring a more relevant and engaging experience without requiring continuous manual effort from administrators. The proposed system contributes by automating gamification personalization, making the process more efficient. It enables the creation of gamified experiences tailored to each user, improving engagement and motivation. Moreover, its flexible and configurable structure can be used in future research.