High failure rates in introductory college science courses, including anatomy and physiology, are common at institutions across the country, and determining the specific factors that contribute to this problem is challenging. To identify students at risk for failure in introductory physiology courses at our open-enrollment institution, an online pilot survey was administered to 200 biology students. The survey results revealed several predictive factors related to academic preparation and prompted a comprehensive analysis of college records of >2,000 biology students over a 5-yr period. Using these historical data, a model that was 91% successful in predicting student success in these courses was developed. The results of the present study support the use of surveys and similar models to identify at-risk students and to provide guidance in the development of evidence-based advising programs and pedagogies. This comprehensive approach may be a tangible step in improving student success for students from a wide variety of backgrounds in anatomy and physiology courses.

Introductory science courses, such as Anatomy and Physiology, are gateway courses to professional degree programs in nursing, radiation technology, and other fields. At the University of Cincinnati Blue Ash College, Anatomy and Physiology I and Fundamentals of Biology I have failure/withdrawal rates of ~35%. In order to identify students at risk for failure, an online survey that collected information regarding students’ high school grades, current college status, personal responsibilities, and math and reading skills was administered to 200 students. Analysis of the data demonstrates statistically significant differences in the academic preparation of passing versus failing students. These results led to a comprehensive study of the college records of more than 2,000 students from a 5‐year period. The analysis shows statistically significant differences between passing and failing students in the following categories: age, high school GPA, college GPA, number of college hours earned, and placement test scores in English and math. Logistic regression analysis using the historical data produces a model with factors that predict student success in these courses. Evidence‐based advising that communicates this information to students could encourage underprepared students to delay entry into gateway courses until they develop the necessary skills, potentially increasing student success.