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

Area of Science:

• Educational Psychology
• Cognitive Science
• Engineering Education

Background:

• Traditional lectures may not fully engage all students.
• Active learning strategies can potentially improve learning outcomes and engagement.
• Understanding physiological responses provides objective measures of engagement.

Purpose of the Study:

• To compare student engagement and physiological responses between active and traditional learning methods.
• To provide a multimodal dataset for further educational and physiological research.
• To investigate the impact of active learning on problem-solving for the One-Dimensional Cutting Stock Problem.

Main Methods:

• 50 engineering students participated in the study.
• Data collected included surveys, electrodermal activity (EDA), heart rate (HR), skin temperature (ST), and behavioral observations.
• A 15-minute video lecture was followed by either a hands-on task (active learning) or no further instruction (control).

Main Results:

• The dataset enables comparison of engagement and physiological data between active and traditional learning conditions.
• Multimodal data allows for in-depth analysis of student responses.
• The active learning group utilized a physical artifact for problem-solving.

Conclusions:

• The dataset supports research into cognitive load, emotional responses, and instructional design.
• Active learning methodologies show promise for enhancing student engagement and understanding complex problems.
• This multimodal dataset offers a valuable resource for diverse research applications in education and physiology.