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Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

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Published on: July 1, 2015

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

José Roberto Dale Luche1, Claudia Regina de Freitas1, Blaha Gregory Correia Dos Santos Goussain1

  • 1Department of Production, São Paulo State University (Unesp), Guaratinguetá, Brazil.

Data in Brief
|June 30, 2025
PubMed
Summary

This study compared active learning to traditional lectures for engineering students. Active learning, involving a hands-on task, showed potential for enhanced engagement and physiological responses during problem-solving.

Keywords:
Educational methodsElectrodermal activity (EDA)Heart rate (HR)Learning engagementNeurophysiological dataPhysiological measurementsSkin temperature (ST)

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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.