Hands-On Mastery in Physiological Signal Processing: A Challenge-Based Learning Approach

Abstract Purpose Teaching and learning statistical signal processing in Biomedical Engineering Degrees poses challenges for both students and teachers. Students often perceive signal processing subjects as demanding and somewhat unattractive, with failure rates several times higher than that of other courses. The aim of this work is to address the following question: Are competitions that present real physiological signal processing problems an effective way to teach the practical skills needed in statistical signal processing? Methods We propose to use a problem-driven activity in which students form groups of two or three students and enter a Kaggle-type competition during the whole semester. The competition consists of a real physiological signal problem along with well-defined metrics to establish the performance of the solutions. We measure the effectiveness of the activity with questionnaires together with sentiment and inductive coding analysis that allows evaluation of the acquisition of competencies. Results According to the results obtained in four consecutive editions, students find the challenge to be a better means than traditional laboratory practices for acquiring practical skills, as well as allowing them to better understand theoretical concepts. Students found the activity challenging and demanding but one of the best activities in the whole degree, since it brings the student closer to real practice as biomedical engineers. Conclusion The results support the hypothesis that this type of competition allows students to gain a deeper understanding of the practical aspects of physiological signal processing courses.