Smart Monitoring and Predictive Dashboard for Educational Laboratory Simulation Platforms

This work presents the development of intelligent monitoring software for educational platforms used in laboratory simulations, developed in Python. It enables tracking of students’ platform usage and recording data, such as access time and usage frequency. In addition, a supervised Artificial Intelligence model based on a decision tree analyzes the collected data and generates predictions regarding the future relevance of the laboratory simulation platforms used. The software includes an interactive dashboard accessible via a web browser, allowing educators to view real-time information and make data-driven decisions to optimize teaching and their didactic strategies. The tests demonstrated high efficiency in data collection and analysis, confirming the feasibility of the proposed solution.