ExamSoft Categories and Machine Learning to Identify Students at High Risk of Failing Board Exams

Session Description

Early identification of students most at risk of failing the COMLEX Level 1 board exam is an important step toward intervening on behalf of those students. Methods utilizing supervised machine learning approaches to predict the category to which something belongs (a spam e-mail vs. a legitimate e-mail for example) based upon some set of variables have become increasingly popular in recent years. Supervised machine learning requires a set of data with the outcome of interest labeled (for example success or failure), along with a set of variables to potentially predict such an outcome.

This session explores the use of ExamSoft categories and machine-learning methods to identify students most at risk of failing COMLEX Level 1 exams at an osteopathic medical school.

Speakers

Matthew K. Pelletier, Ph.D. Director of Assessment and Outcomes, Liberty University College of Osteopathic Medicine