Fostering Engagement and Interdisciplinary Skills Through Air Quality Monitoring and Data Analytics in Engineering Education
Academic Article in Scopus
This work-in-progress project presents an innovative and interdisciplinary educational experience that combines real-time air quality monitoring, environmental chemistry, data analytics, and machine learning to enhance student engagement and learning outcomes. Designed for students in the fifth semester of the Chemical Engineering and Engineering in Data Science and Mathematics programs at Tecnológico de Monterrey, Mexico, the initiative fosters a deep understanding of both the technical and societal dimensions of air pollution. Students learn how these monitors are built and they deploy them at their outdoor houses. four low-cost IoT-based air quality monitors capable of measuring ozone, nitrogen dioxide, carbon monoxide, and particulate matter are used. The monitors provide community-relevant data on outdoor air pollution. Students learn about statistical modeling, geospatial analysis, and machine learning techniques using R and Python to identify trends and forecast concentrations. The project integrates photochemical kinetics with data science methods. Students strengthen their technical, analytical, and collaborative skills. Pre/post surveys and engagement scales are employed to assess impact on learning gains and student motivation. This educational innovation embeds sustainability, data literacy, and social responsibility into core engineering curricula. Preliminary findings suggest improved engagement and a greater sense of purpose in applying technical knowledge to address environmental challenges.
