Effective online large-group teaching in health professions education

Abstract

Online large-group teaching (OLGT), employed to reach a large group of learners in separate physical locations, allows asynchronous learning and facilitates social distancing. While online large-groups can be a powerful and resource-lean means of health professions education, it has challenges and potential pitfalls that may affect the learning process and outcomes. Through a sociomateriality framework, this article describes strategies for effective online large-group teaching in health professions education in three key strands. Firstly, to optimize learning, OLGT sessions should match learning needs with appropriate OLGT platforms, incorporate strategies to sustain learner attention, and accommodate learners of different abilities. Secondly, to develop a learning culture, OLGT must not only focus on cognitive aspects of learning but also build a community of practice, nurture digital professionalism and professional identity. Thirdly, we discuss the avoidance of pitfalls such as cognitive overload of both tutors and learners, technical issues and security risks, mitigating inequities in access to online learning, and the use of program evaluation to plan for sustained improvements. We conclude with a case vignette that discusses the challenges of OLGT and the application of the above strategies in a teaching scenario.

Keywords: E-learning; Large-group teaching; Online learning; Sociomateriality; Webinar.

Fig. 1.. Schematic Illustration of Online Large-Group Health Professions Education from a Sociomateriality Viewpoint

How does large group online health professions education take place? (A) Didactic model: one-way information flow from teacher to learner. (B) Interactive model: two-way interactions between teacher & learners through use of elements such as polls and quizzes. (C) Sociomateriality model: learning characterized by complex interactions between learners, teacher, materials and environments.

Fig. 2.. Example of a Simplified Input-Process-Output Model