Towards Meaningful Learning in Analysis of Biomedical Signals and Systems through Google Colab Notebooks
摘要
The Biomedical Engineering (BME) program at our institution is structured into three stages: Fundamentals, Training, and Specialization. The transition from Fundamentals to Training is particularly challenging for students. Instructors often struggle to provide meaningful learning experiences, focusing primarily on mathematical analysis without fostering integration of theoretical knowledge with practical applications. This gap highlights the need for instructional strategies that promote meaningful learning.
Novel InitiativeTo address these challenges, an instructional strategy based on meaningful learning principles was developed and implemented using Google Colab notebooks in the Biomedical Signals and Systems (BSS) course. The initiative was executed in three stages: (1) a pilot study to evaluate the feasibility of notebook design aligned with course modules, (2) redesign of the notebooks based on qualitative and quantitative feedback, and (3) application and evaluation of the redesigned notebooks in a controlled study. The intervention cohort using the redesigned notebooks demonstrated significantly higher performance on standardized evaluations compared to the control cohort, indicating the effectiveness of the approach.
ReflectionThe initiative successfully improved student engagement, satisfaction, and performance in the BSS course, fostering meaningful learning and strengthening critical STEM-related skills. Students appreciated the practical, real-world applications and collaborative learning opportunities provided by Google Colab notebooks. Grounded in Ausubel’s Theory of Meaningful Learning, this initiative demonstrates the potential of interactive and collaborative tools like Google Colab to bridge the gap between abstract concepts and practical applications in BME education.