Predicting Student Success in a Major’s Introductory Biology Course via Logistic Regression Analysis of Scientific Reasoning Ability and Mathematics Scores

• Published in: Research in science education (Australasian Science Education Research Association) Vol. 48; no. 1; pp. 151 - 163
• Main Authors: Thompson, E. David, Bowling, Bethany V., Markle, Ross E.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2018; Springer; Springer Nature B.V
• Subjects: Academic achievement; Biological effects; Biology; College Attendance; College Entrance Examinations; College Students; Decision making; Education; Grade Point Average; Logical Thinking; Majors (Students); Mathematical analysis; Mathematics Achievement; Mathematics Education; Predictor Variables; Quality; Reasoning; Regression (Statistics); Regression analysis; Science Education; Science Instruction; Scores; Students; Success; Support services; Thinking Skills; Student success; Retention; Scientific reasoning; First-year students; Introductory biology; Predictive model
• ISSN: 0157-244X; 1573-1898
• DOI: 10.1007/s11165-016-9563-5

Studies over the last 30 years have considered various factors related to student success in introductory biology courses. While much of the available literature suggests that the best predictors of success in a college course are prior college grade point average (GPA) and class attendance, faculty often require a valuable predictor of success in those courses wherein the majority of students are in the first semester and have no previous record of college GPA or attendance. In this study, we evaluated the efficacy of the ACT Mathematics subject exam and Lawson’s Classroom Test of Scientific Reasoning in predicting success in a major’s introductory biology course. A logistic regression was utilized to determine the effectiveness of a combination of scientific reasoning (SR) scores and ACT math (ACT-M) scores to predict student success. In summary, we found that the model—with both SR and ACT-M as significant predictors—could be an effective predictor of student success and thus could potentially be useful in practical decision making for the course, such as directing students to support services at an early point in the semester.