  An Objective Structured Practical Examination (OSPE) is an effective and
robust, but resource-intensive, means of evaluating anatomical knowledge. Since
most OSPEs employ short answer or fill-in-the-blank style questions, the format
requires many people familiar with the content to mark the exams. However, the
increasing prevalence of online delivery for anatomy and physiology courses
could result in students losing the OSPE practice that they would receive in
face-to-face learning sessions. The purpose of this study was to test the
accuracy of Decision Trees (DTs) in marking OSPE questions as a potential first
step to creating an intelligent, online OSPE tutoring system. The study used
the results of the winter 2020 semester final OSPE from McMaster University's
anatomy and physiology course in the Faculty of Health Sciences (HTHSCI
2FF3/2LL3/1D06) as the data set. Ninety percent of the data set was used in a
10-fold validation algorithm to train a DT for each of the 54 questions. Each
DT was comprised of unique words that appeared in correct, student-written
answers. The remaining 10% of the data set was marked by the generated DTs.
When the answers marked by the DT were compared to the answers marked by staff
and faculty, the DT achieved an average accuracy of 94.49% across all 54
questions. This suggests that machine learning algorithms such as DTs are a
highly effective option for OSPE grading and are suitable for the development
of an intelligent, online OSPE tutoring system.
