Abstract
The study of user logs plays a crucial role in understanding user behavior and preferences in various online environments. By analyzing user logs, researchers can gain valuable insights into how users interact with a system and make informed decisions on system improvements. They can also assess the effectiveness of different features and functionalities. In the field of game design, the exploration of user logs becomes even more important as it provides valuable information on player motivations, preferences, and gameplay patterns. This research explores the impact of Bartle Taxonomy on user behavior analysis through a Game with a Purpose (GWAP) named "BartleZ."By analyzing user logs and decisions within the game, BartleZ aims to determine the dominant player type according to the Bartle Taxonomy classification. This research also investigates how different player types engage with the game and the implications for user experience design. © 2025 Elsevier B.V., All rights reserved.

Abstract
The current surge in the deployment of web applications underscores the need to consider users' individual preferences in order to enhance their experience. In response to this, an innovative approach is emerging that focuses on the detailed analysis of interaction data captured by web browsers. This data, which includes metrics such as the number of mouse clicks, keystrokes, and navigation patterns, offers insights into user behaviour and preferences. By leveraging this information, developers can achieve a higher degree of personalization in web applications, particularly in the context of interactive elements such as online games. This paper presents the WebTraceSense project, which aims to pioneer this approach by developing a framework that encompasses a backend and frontend, advanced visualization modules, a DevOps cycle, and the integration of AI and statistical methods. The backend of this framework will be responsible for securely collecting, storing, and processing vast amounts of interaction data from various websites. The frontend will provide a user-friendly interface that allows developers to easily access and utilize the platform’s capabilities. One of the key components of this framework is the visualization modules, which will enable developers to monitor, analyse, and interpret user interactions in real-time, facilitating more informed decisions about user interface design and functionality. Furthermore, the WebTraceSense framework incorporates a DevOps cycle to ensure continuous integration and delivery, thereby promoting agile development practices and enhancing the overall efficiency of the development process. Moreover, the integration of AI methods and statistical techniques will be a cornerstone of this framework. By applying machine learning algorithms and statistical analysis, the platform will not only personalize user experiences based on historical interaction data but also infer new user behaviours and predict future preferences. In order to validate the proposed components, a case study was conducted which demonstrated the usefulness of the WebTraceSense framework in the creation of visualizations based on an existing dataset.