Effectiveness of blended learning versus lectures alone on ECG analysis and interpretation by medical students

Abstract

Background Most medical students lack confidence and are unable to accurately interpret ECGs. Thus, better methods of ECG instruction are being sought. Current literature indicates that the use of e-learning for ECG analysis and interpretation skills (ECG competence) is not superior to lecture-based teaching. We aimed to assess whether blended learning (lectures supplemented with the use of a web application) resulted in better acquisition and retention of ECG competence in medical students, compared to conventional teaching (lectures alone). Methods Two cohorts of fourth-year medical students were studied prospectively. The conventional teaching cohort (n = 67) attended 4 hours of interactive lectures, covering the basic principles of Electrocardiography, waveform abnormalities and arrhythmias. In addition to attending the same lectures, the blended learning cohort (n = 64) used a web application that facilitated deliberate practice of systematic ECG analysis and interpretation, with immediate feedback. All participants completed three tests: pre-intervention (assessing baseline ECG competence at start of clinical clerkship), immediate post-intervention (assessing acquisition of ECG competence at end of six-week clinical clerkship) and delayed post-intervention (assessing retention of ECG competence 6 months after clinical clerkship, without any further ECG training). Diagnostic accuracy and uncertainty were assessed in each test. Results The pre-intervention test scores were similar for blended learning and conventional teaching cohorts (mean 31.02 ± 13.19% versus 31.23 ± 11.52% respectively, p = 0.917). While all students demonstrated meaningful improvement in ECG competence after teaching, blended learning was associated with significantly better scores, compared to conventional teaching, in immediate (75.27 ± 16.22% vs 50.27 ± 17.10%, p < 0.001; Cohen’s d = 1.58), and delayed post-intervention tests (57.70 ± 18.54% vs 37.63 ± 16.35%, p < 0.001; Cohen’s d = 1.25). Although diagnostic uncertainty decreased after ECG training in both cohorts, blended learning was associated with better confidence in ECG analysis and interpretation. Conclusion Blended learning achieved significantly better levels of ECG competence and confidence amongst medical students than conventional ECG teaching did. Although medical students underwent significant attrition of ECG competence without ongoing training, blended learning also resulted in better retention of ECG competence than conventional teaching. Web applications encouraging a stepwise approach to ECG analysis and enabling deliberate practice with feedback may, therefore, be a useful adjunct to lectures for teaching Electrocardiography.

Curiosity in Online Video Concept Learning and Short-Term Outcomes in Blended Medical Education

“…Blended learning appears to be more effective than or at least as effective as non-blended instruction for knowledge acquisition in health professions (31,32). When designed well, blended learning courses in medicine can facilitate students to improve themselves in self-learning, understanding, and problem solving, ultimately enhancing their learning efficiency (33)(34)(35). A potential pitfall of blended learning is the isolated nature of e-learning if the student interacts on an individual basis with the computer and not with a peer group, which can be overcome in part by online engagement through webinars and discussion boards (36).…”

Implementation of Blended Learning to Improve Motivation and Learning Outcomes of Pre-Service Early Childhood Teachers

“…Then the experimental class was given treatment with blended learning using several face-to-face platforms. At the same time, the control class used conventional learning in full online without any face-to-face platform assistance (Viljoen et al, 2020). After being given treatment, both classes were given a post-test to determine the improvement in learning outcomes for pre-service early childhood teachers.…”

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals

“…Correct and quick diagnosis of cardiac arrhythmias is very important for the prevention and treatment of heart diseases [ 6 ]. Misinterpretations of ECGs may lead to incorrect clinical decisions and adverse outcomes [ 8 ]. Interpretation by ECG requires sufficient expertise and training, and the observer’s knowledge directly influences the accuracy, so applying automatic diagnosis of arrhythmias using ECG signals, can be useful in preventing human errors [ 3 , 9 , 10 ].…”