Hooper, S.E.; Hecker, K.G.; Artemiou, E. Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators. Vet. Sci.2023, 10, 537.
Hooper, S.E.; Hecker, K.G.; Artemiou, E. Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators. Vet. Sci. 2023, 10, 537.
Hooper, S.E.; Hecker, K.G.; Artemiou, E. Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators. Vet. Sci.2023, 10, 537.
Hooper, S.E.; Hecker, K.G.; Artemiou, E. Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators. Vet. Sci. 2023, 10, 537.
Abstract
Machine learning (ML) offers potential opportunities to enhance the learning, teaching and assessments within veterinary medical education including but not limited to assisting with admissions processes as well as student progress evaluations. The purpose of this primer is to assist veterinary educators in appraising and potentially adopting these rapid upcoming advances in data science and technology. In the first section, we introduce ML concepts and highlight similarities/differences between ML and classical statistics. In the second section, we provide a step-by-step worked example using simulated veterinary student data to answer a hypothesis driven question. Python syntax with explanations is provided within the text to create a random forest ML prediction model and within each step, specific considerations such as how to manage incomplete student records are highlighted when applying ML algorithms within the veterinary education field. The results from the simulated data demonstrate how decisions by the veterinary educator during ML model creation may impact the most important features contributing to the model. These results highlight the need for the veterinary educator to be fully transparent during the creation of ML models and future research is needed to establish guidelines for handling data not missing at random in medical education, and preferred methods for model evaluation.
Keywords
machine learning; veterinary medical education; random forest; medical education; artificial intelligence; Python; R; veterinary educators; educational data mining; learning analytics
Subject
Medicine and Pharmacology, Veterinary Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
