Abstract:

Introduction:
Developing and adapting laboratory experiments and useful practical resources is critical for understanding complex mechanisms while keeping accurate terminology in medical and STEM courses. Likewise, developing appropriate tools for tracking the learning process being able to adept teaching resources and methodologies is of utmost interest in Higher-Education, either while following blended or face-to-face teaching strategies. This is especially relevant while teaching complex concepts holding a challenge to integrate and visualize. The autonomic nervous system balance has a profound control over neuro-modulation cardiovascular (CVC) adaptation. As it is well known that dynamic control of the CVC system by autonomic nerves may be monitored in real time by heart rate (HR) and blood pressure acquisition, we propose a set of interventions and in-silico tools for aiding students understanding such modifications while monitoring the learning process. We surmise that such interventions may aid students:
1) to comprehensively understand major CVC control mechanisms by using a custom tool for analysing well-known adaptations as well as quantifying dynamically standard biomarkers of HR variability (HRV) for interpretation;
2) to understand CVC adaptation under physiological and stress conditions by in-silico predictive simulation. Additionally, a strategy for tracking learning analytics is also tested for optimizing and redirecting efforts, contents and resources.

Methods:
We used interactive Q/A feedback web-apps for tracking fundamentals’ acquisition before and after each lesson. Theoretical fundamentals of automatic control and CVC adaptation were provided using a blended-learning strategy combining face-2-face and online discussions, and a platform for collecting support materials, interaction and content reinforcement before practical workshop sessions. We tracked the learning process by using an integrated follow-up dashboard with nested online feedback forms, as well as deliverables gathered during the course practical sessions. Specialized software was used for dynamic quantification of autonomic markers, visualization, interpretation and discussion. Easy-to-perform interventions using Holters allowed recording physiological activity at baseline rest either for 20min, or as a response to well-known manoeuvres and autonomic tests for later analyses and interpretation. Short-term HRV was analysed by students, comparing standard measurements in time, time-frequency and non-linear markers for physiological interpretation. Furthermore, in-silico computational simulations were used as learning environment to understand CVC adaptations under physiological conditions.

Results:
Basic automatic control and specific medical terminology were assessed via interactive Q/A platforms. Pre-lab questionnaires showed heterogeneous learning before practical interventions while showing after overall enhanced learning perception on core and integrative concepts, and improved instrumental skills. Students found interventions and interactive tools very useful and self-explanatory for understanding such complex concepts. The learning analytics platform allowed identifying key concepts to be reinforced.

Conclusion:
We evaluated a methodology and interactive scientific tools, for tracking learning and acquisition of complex concepts, such as, autonomic control evaluation, by dynamic HRV interpretation, and, cardiovascular adaptation.

Keywords:

Technology enhanced learning, HRV analyses, ANS control, CVC adaptation, ICT-Tools, learning analytics.