User attention management has become a growing concern in recent years, largely due to the consolidation of remote and hybrid work models after widespread adoption during the COVID-19 pandemic. This abrupt transition to virtual environments has introduced new challenges, particularly regarding the difficulty of maintaining focus on personal, professional, and academic tasks. Regardless of the context, attention is crucial for task performance, especially in digital environments that are more susceptible to distractions. These challenges can impact time management, task balance, and overall well-being, highlighting the need to understand the factors influencing attention and to develop strategies to mitigate distractions. This study aims to support attentional focus analysis in computational environments through continuous user behavior monitoring, leveraging multimodal data and visualization techniques. To achieve this, a literature review explored studies utilizing webcam-based data—an emerging tool for behavioral research—mapping the state of the art and identifying gaps and research opportunities. Based on the identified needs, we propose a model for attentional analysis, considering the most relevant data types for this field and the leading open-source tools for feature extraction. To validate this approach, we developed the \textit{AttentionVis Dashboard}, a prototype designed to detect moments of attention and distraction, as well as different attention types, with the goal of fostering self-regulation. The prototype was tested in a controlled environment following a structured protocol to ensure the consistency of the results. The findings indicate that it effectively distinguishes attentional patterns. However, some limitations were also identified, revealing areas for improvement in future iterations.
https://davint-attentionvis-dashboard.streamlit.app
We are members of the Data Visualization and Interaction Lab (DaVInt) at PUCRS:
- Isabel H. Manssour -- Researcher and Professor Coordinator of DaVInt.
- Milene S. Silveira -- Researcher and Professor.
- Cassiano S. Souza -- Master Student in Computer Science -- 2023-current.