Background: Medical students value teaching by junior doctors and find it comparable to consultant-led teaching. Although several junior doctor-led teaching programmes have been developed, there is insufficient information in the literature to guide junior doctors planning on developing such programmes. Aim: This article gives junior doctors 12 practical tips on how they might develop and run successful teaching programmes for medical students. Results: The 12 tips are (1) Clearly define the scope of your programme, (2) Ensure student-defined learning goals are included at an early stage, (3) Inform and involve your fellow junior doctors in teaching, (4) Plan teaching rotas in advance, (5) Learn to teach effectively by attending courses, (6) Promote your programme to medical students as widely as possible, (7) Use varied and interactive teaching methods, (8) Establish rapport with students, (9) Include assessment as part of the teaching programme, (10) Seek feedback from attendees and senior faculty, (11) Establish rules for tutorials and (12) Secure formal recognition for your scheme. Conclusions: These 12 tips may help junior doctors to develop and manage successful teaching programmes. It may also be a useful guide for senior faculty advising junior doctors who aspire to establish such teaching programmes.
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 2 years ago
Active-learning pedagogies have been repeatedly demonstrated to produce superior learning gains with large effect sizes compared with lecture-based pedagogies. Shifting large numbers of college science, technology, engineering, and mathematics (STEM) faculty to include any active learning in their teaching may retain and more effectively educate far more students than having a few faculty completely transform their teaching, but the extent to which STEM faculty are changing their teaching methods is unclear. Here, we describe the development and application of the machine-learning-derived algorithm Decibel Analysis for Research in Teaching (DART), which can analyze thousands of hours of STEM course audio recordings quickly, with minimal costs, and without need for human observers. DART analyzes the volume and variance of classroom recordings to predict the quantity of time spent on single voice (e.g., lecture), multiple voice (e.g., pair discussion), and no voice (e.g., clicker question thinking) activities. Applying DART to 1,486 recordings of class sessions from 67 courses, a total of 1,720 h of audio, revealed varied patterns of lecture (single voice) and nonlecture activity (multiple and no voice) use. We also found that there was significantly more use of multiple and no voice strategies in courses for STEM majors compared with courses for non-STEM majors, indicating that DART can be used to compare teaching strategies in different types of courses. Therefore, DART has the potential to systematically inventory the presence of active learning with ∼90% accuracy across thousands of courses in diverse settings with minimal effort.
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