Physiological and behavioral data on active and traditional learning in engineering education.

This dataset comprises information gathered from an experiment designed to examine learning engagement and physiological responses in students exposed to different teaching methodologies. The dataset includes records from 50 engineering students, ensuring a diverse representation of participants. It contains survey responses capturing self-reported engagement, neurophysiological measurements such as electrodermal activity (EDA), heart rate (HR), and skin temperature (ST), along with observational records documenting behavioral interactions during the learning process. Participants were first exposed to a 15-minute video lecture introducing the One-Dimensional Cutting Stock Problem, its industrial applications, and heuristic algorithms such as Next Fit, First Fit, and First Fit Decreasing. In the active learning condition, students engaged in a hands-on problem-solving task, using a physical artifact made of bamboo and PVC to develop and optimize cutting plans for a predefined set of pieces, following a heuristic approach before searching for an optimal solution. The dataset is structured to facilitate analyses comparing active and traditional instructional methods. Given its multimodal nature, it can be reused in various educational and physiological research contexts, including investigations into cognitive load, emotional responses, and instructional design optimization.