The Royal Australian and New Zealand College of Ophthalmologists - RANZCO (AUSTRALIA)
About this paper:
Appears in: INTED2023 Proceedings
Publication year: 2023
Pages: 1-10
ISBN: 978-84-09-49026-4
ISSN: 2340-1079
doi: 10.21125/inted.2023.0003
Conference name: 17th International Technology, Education and Development Conference
Dates: 6-8 March, 2023
Location: Valencia, Spain
For decades, medical education has used Multiple Choice Questions (MCQs) in undergraduate, postgraduate, and specialist training programs. With the development of Learning Management Systems (LMS) in the early 1990s, educators adapted paper-based MCQs for formative and summative assessments. Technology allowed automated feedback, question shuffling, instant marking, and ‘branching’ based on student responses. With the advent of e-learning authoring tools in the early 2000s, it became possible to create interactive online tests with images, animations, videos, drag-and-drop elements, blanks to fill in, and hotspots. Effective MCQ writing involves understanding educational concepts like learning taxonomies, constructive alignment, approaches to learning, cognitive load, and student motivation to learn. It is also essential when structuring a question to avoid ambiguity and to have the imagination to write MCQs that measure application of knowledge. Whether a basic or advanced topic, it is possible to design MCQs that measure higher-order thinking that require the student to apply their knowledge rather than simply recalling it. MCQs with hypothetical scenarios can measure higher-order thinking and promote deep learning. However, preparing the students for this type of examination is essential to enhance their learning experience. This article discusses the theoretical considerations involved in writing MCQs for medical education which encourage deep learning and improve the student learning experience.
Assessments, MCQs, higher-order thinking, deep learning, medical education.